Next Article in Journal
Collaborative Multimodal Writing via Google Docs: Perceptions of French FL Learners
Next Article in Special Issue
The Phonetics of Tone and Voice Quality Interactions in Sylheti
Previous Article in Journal
Social Networks: A Source of Lexical Innovation and Creativity in Contemporary Peninsular Spanish
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Languages
Volume 6
Issue 3
10.3390/languages6030139
languages-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Fundamental Frequency and Phonation Differences in the Production of Stop Laryngeal Contrasts of Endangered Shina

by
Qandeel Hussain
1,2
1
Department of Linguistics, Faculty of Arts and Science, University of Toronto, St. George Campus, Toronto, ON M5S 3G3, Canada
2
Department of English (Linguistics Program), College of Humanities and Social Sciences, North Carolina State University, Raleigh, NC 27695, USA
Languages 2021, 6(3), 139; https://doi.org/10.3390/languages6030139
Submission received: 9 April 2021 / Revised: 9 August 2021 / Accepted: 11 August 2021 / Published: 17 August 2021
(This article belongs to the Special Issue Exploring the Interaction between Phonation and Prosody)
Browse Figures
Versions Notes

Abstract

:
Shina is an endangered Indo-Aryan (Dardic) language spoken in Gilgit, Northern Pakistan. The present study investigates the acoustic correlates of Shina’s three-way stop laryngeal contrast across five places of articulation. A wide range of acoustic correlates were measured including fundamental frequency (F0), spectral tilt (H1*-H2*, H1*-A1*, H1*-A2*, and H1*-A3*), and cepstral peak prominence (CPP). Voiceless aspirated stops were characterized by higher fundamental frequency, spectral tilt, and cepstral peak prominence, compared to voiceless unaspirated and voiced unaspirated stops. These results suggest that Shina is among those languages which have a raising effect of aspiration on the pitch and spectral tilt onsets of the following vowels. Positive correlations among fundamental frequency, spectral tilt, and cepstral peak prominence were observed. The findings of this study will contribute to the phonetic documentation of endangered Dardic languages.

1. Introduction

Shina is an endangered Indo-Aryan (Dardic) language spoken in Gilgit, Northern Pakistan. There is a three-way laryngeal contrast in Shina stops (e.g., voiceless unaspirated /p/, voiceless aspirated /ph/, and voiced unaspirated /b/) at five places of articulation (bilabial, dental, retroflex, palatal, and velar; Hussain 2018; Radloff 1999). From a typological perspective, voiceless aspirated stops have been described to have either a raising effect on fundamental frequency (F0) and spectral tilt or a lowering effect (see below). A wide range of studies have been conducted on the grammatical, phonological, and sociolinguistic aspects of Dardic languages (Backstrom and Radloff 1992; Cacopardo and Cacopardo 2001; Liljegren 2017). However, Shina still lacks a detailed phonetic description. The current study investigates F0, spectral tilt (H1*-H2*, H1*-A1*, H1*-A2*, and H1*-A3*), and cepstral peak prominence (CPP) of the rich laryngeal and place contrasts of Shina. In particular, I investigate whether aspirated stops have a raising effect on F0 and spectral tilt onsets. Moreover, relationships among all the acoustic measures are explored using correlations.
The paper is organized as follows. Section 1.1 presents a brief typological survey of the laryngeal contrasts found in Dardic languages. Section 1.2 discusses the acoustic correlates of laryngeal and place contrasts. Section 2 outlines the methods and procedures used in the current study. The main findings are presented in Section 3. In the end, Section 4 summarizes the results and highlights the implications for the general literature on laryngeal typology, tonogenesis, and phonationgenesis.

1.1. Typology of Laryngeal Contrasts in Dardic Languages

The term Dardic is generally used to refer to a group of Indo-Aryan languages spoken in the Northwestern regions of South Asia (spanning the border areas of Afghanistan, Pakistan, and India, including both the Pakistani- and Indian-administered areas of the Kashmir region; Figure 1). There is an ongoing debate whether Dardic should be considered as a sub-family of Indo-Aryan or Nuristani languages (Morgenstierne 1932; Strand 1973). However, recent studies associate Dardic with Indo-Aryan languages (Bashir 2003; Liljegren and Akhunzada 2017; Strand 2001b). Dardic languages are characterized by two-way, three-way, or four-way laryngeal contrasts. Figure 1 presents geographical distribution of Dardic languages and the number of laryngeal contrasts in each language. Bateri is the only Dardic language which contrasts two laryngeal categories (e.g., voiceless unaspirated /p/ and voiced unaspirated /b/; Strand 2001a).1 Three laryngeal categories (voiceless unaspirated /p/, voiceless aspirated /ph/, and voiced unaspirated /b/) can be found in Dameli (Morgenstierne 1942; Perder 2013), Gawarbati (Bashir 2003), Kalam Kohistani (also called Garwi, Gawri, or Bashkarik: Baart 1999; Morgenstierne 1940), Kashmiri (Wali and Koul 1997),2 Kalkoti (Liljegren 2013), Khowar (Liljegren and Khan 2017), Kundal Shahi (Rehman and Baart 2005), Shina (Radloff 1999), and Ushojo (Decker 1992). Dardic languages which have a four-way laryngeal contrast (voiceless unaspirated /p/, voiceless aspirated /ph/, voiced unaspirated /b/, and voiced aspirated /bɦ/) include Indus Kohistani (Bashir 2003), Kalasha (Bumburate, Birir, and Rumbur dialects; Hussain and Mielke 2020; Kochetov et al. 2020), Palula (Liljegren and Haider 2009; author’s own fieldwork), Sawi, and Torwali (Bashir 2003).
The status of voiced aspirates is still controversial in many of the above-mentioned Dardic languages (Bashir 2003). Morgenstierne (1932) noted that aspiration is preserved in voiced stops of Palula but only in word-initial position (confirmed by the current author’s own fieldwork as well). However, in word-medial and word-final positions, voiced aspirates are characterized by little or no clear aspiration interval. Language contact might be one of the factors contributing to this sound change. Indus Kohistani, Kalasha, Palula, Sawi, and Torwali are surrounded by languages which have two-way (Eastern Kataviri and Pashto) or three-way (Gujari, Khowar, and Pashai) laryngeal contrasts. A high level of bilingualism is also prevalent in the Northwestern regions where speakers are fluent in at least two languages of the area. Thus, it appears that at some point, Dardic languages may completely lose the aspiration contrast in voiced stops, either due to language contact or internal sound change. Detailed acoustic-phonetic studies are needed to confirm the number of laryngeal contrasts found in Dardic languages.

1.2. Acoustic Correlates of Laryngeal Contrasts

In the current phonetic literature, a number of acoustic measures have been used to investigate the laryngeal contrasts. Fundamental frequency (F0) of the following vowel onsets has been extensively used to examine the laryngeal contrasts (Chen 2011; Hombert et al. 1979; House and Fairbanks 1953; Kirby and Ladd 2016; Ladd and Schmid 2018; Lee et al. 2019). Voiced stops generally exhibit lower F0 onsets than voiceless stops (Hombert et al. 1979; Schertz and Khan 2020). Shimizu (1989) conducted a cross-linguistic study and noted that voiced stops of Japanese, Burmese, Thai, and Hindi are characterized by lower F0 onsets than voiceless stops. These patterns can be observed in Figure 2 which presents the F0 onsets of the three laryngeal categories of Burmese and Thai.3 Some studies have also observed the effect of aspiration on F0 onsets but there appears to be inconsistency in the reported results. Voiceless unaspirated stops of Burushaski (Hussain 2021), Cantonese, Mandarin (Luo et al. 2016), Kalasha (Hussain and Mielke 2020), Marathi (Dmitrieva and Dutta 2020), and Shanghai Chinese (Chen 2011) showed higher F0 onsets than voiceless aspirated stops. In contrast, voiceless unaspirated stops of Chru (Brunelle et al. 2020), Madurese (Misnadin 2016), and Thai (Shimizu 1989) entailed lower F0 onsets than voiceless aspirated stops. Dzongkha’s voiceless unaspirated and aspirated categories showed high F0 onsets, indicating no clear differences (Lee and Kawahara 2018). Ohde (1984) also showed that voiceless unaspirated and voiceless aspirated stops of English are not distinguished by F0 onsets.
If we look at the typology of obstruent voicing and its effects on F0 onsets, three general patterns emerge: (a) languages which have a raising effect on the F0 onsets of voiceless aspirated stops (Chru, Madurese, and Thai); (b) languages which have a lowering effect on the F0 onsets of voiceless aspirated stops (Burushaski, Cantonese, Hindi, Kalasha, Mandarin, Marathi, and Shanghai Chinese); and (c) languages which show no clear or large differences in the F0 onsets of voiceless unaspirated and aspirated stops (Burmese, Dzongkha, and English; see also Kirby and Hyslop 2019 which showed raising effects on the F0 onsets of voiceless aspirated stops in male speakers of Dzongkha whereas female speakers showed the opposite pattern). It should also be noted that F0 onsets are generally used to investigate the laryngeal contrasts. Lai et al. (2009) showed minor but significant effects of place on F0 onsets in Taiwanese. It is not clear which articulatory forces may be involved and resulted in these place effects. It is also worth noting that the apparent differences in F0 may arise due to the number of laryngeal and place categories found in a language, and whether a language has undergone the processes of tonogenesis and registrogenesis, and the type of pitch and/or focus contrasts (Brunelle et al. 2019; Tan et al. 2019; Wayland and Jongman 2002).
Spectral tilt/balance has also been used to investigate voicing and aspiration contrasts across diverse languages (Esposito and Khan 2020; Garellek 2019; Iseli et al. 2007). There are four spectral tilt measures: (a) H1*-H2* (the amplitude difference between the first [H1] and second [H2] harmonic); (b) H1*-A1* (the amplitude difference between the first harmonic [H1] to the amplitude of F1); (c) H1*-A2* (the amplitude difference between the first harmonic [H1] to the amplitude of F2); and (d) H1*-A3* (the amplitude difference between the first harmonic [H1] to the amplitude of F3) (Garellek 2019). Aspirated stops have higher spectral tilt at the onset of following vowels than non-aspirated stops (Brunelle et al. 2019; Esposito and Khan 2012; Gao et al. 2020; Seyfarth and Garellek 2018). For example, in Dzongkha (Kirby and Hyslop 2019), Hindi/Urdu (Schertz and Khan 2020, Gujarati (Esposito et al. 2020), and Marathi (Berkson 2019; Dmitrieva and Dutta 2020), aspirated stops have been reported to show higher H1*-H2* and H1*-A1* at the onset of following vowels than voiceless unaspirated stops. In terms of voicing, there were no clear differences in H1*-H2* onsets of voiceless unaspirated and voiced unaspirated stops in Hindi/Urdu (Schertz and Khan 2020). Voiced unaspirated and voiceless aspirated stops of Yerevan Armenian were characterized by identical H1*-H2* (Seyfarth and Garellek 2018). In addition to spectral tilt, cepstral peak prominence (CPP), which captures any form of noise in an acoustic signal (breathiness and/or aspiration), has been reported to distinguish modal and breathy vowels (Garellek and Keating 2011). Since breathiness results in less distinct harmonics, the CPP values are expected to be much lower in vowels following aspirated stops (Garellek 2019; Misnadin 2016).
A number of studies have reported correlations among F0 and spectral tilt. Iseli et al. (2007) observed positive correlations between H1*-H2* and F0 for one group of talkers (older males). Koreman (1996) noted that an increase in the tension of the cricothyroid muscle in the larynx results in an increase of F0 and H1*-H2* (see also Kreiman et al. 2012 and Keating and Esposito 2007 for further discussion). The results of these studies suggest that there may be correlations among F0 and spectral tilt across laryngeal categories which differ on the dimensions of aspiration and/or voicing.
The aim of the current study is to investigate F0, spectral tilt (H1*-H2*, H1*-A1*, H1*-A2*, and H1*-A3*), and CPP of the rich laryngeal contrasts (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) and places of articulation (bilabial, dental, retroflex, palatal, and velar) of Shina, an endangered Dardic language spoken in Gilgit, Northern Pakistan. Relationships among F0, spectral tilt, and CPP are further explored using correlations.

2. Methods

2.1. Speakers

Five Shina speakers (range: 18–32 years; mean age: 24.6 years) were recruited from Gilgit, Northern Pakistan. All the speakers were healthy adults and had no speech and/or hearing disabilities. The sample was homogeneous. The speakers learned Shina as their first language and could also speak Urdu, a national language and lingua franca of Pakistan. All the speakers were screened before including in the study. Only those speakers who regularly spoke Shina with their family and friends were selected. Speakers who were predominantly Urdu speakers (i.e., most of the time they used Urdu) were not included in the study.

2.2. Speech Materials and Recording Procedure

A wordlist of Shina was created, where word-initial C represented a stop consonant (/p ph b t̪ t̪h d̪ ʈ ʈh ɖ ʧ ʧh ʤ k kh ɡ/), followed by vowel /a/ (each word had a CV syllable structure; see Table A1 in Appendix A). As the quality of vowels can affect fundamental frequency and other acoustic measures, nonsense words were created to better control for the phonological environment and vowel contexts. The nonsense wordlist was created in consultation with the native Shina speakers. The speakers were asked whether the selected words occur in their language or have they used any of these words in their everyday conversations. All the target words were balanced across consonant types and places of articulation. The target stops were investigated in word-initial position as this is the only position where full set of laryngeal and place contrasts are found. Before the start of the recording, speakers were familiarized with the wordlist and were asked to produce the words as they are speaking Shina using their natural speech tempo. A portable Zoom digital voice recorder with a built-in microphone was placed near the speakers to make audio recordings (44.1 kHz, encoded in 16-bit; recorder was placed around 1.5 feet from the speakers’ mouth). It was made sure that there was no background noise during the recordings. All the words were produced in citation form. Each word was repeated five times (5 speakers × 15 words × 5 repetitions of each word = 375 tokens).

2.3. Acoustic and Statistical Analyses

A total of 375 tokens were segmented in Praat (Boersma and Weenink 2007; 11 tokens were excluded due to mispronunciations and missing repetitions; 364 tokens were used for the final analysis). Fundamental frequency, spectral tilt (H1*-H2*, H1*-A1*, H1*-A2*, and H1*-A3*; asterisks indicate that the measurements were corrected for the boosting effects of formants; Iseli et al. 2007), and CPP were measured from the onset of following vowels, using PraatSauce (Kirby 2018b). The most important information about a laryngeal category and place of articulation is found right after the release of a consonant (i.e., near the onset or beginning of the following vowel). Therefore, all the measurements are based on the onset which refers to a single time point at the beginning of the vowel /a/.
The statistical analyses of the data were conducted in R (R Core Team 2013). Separate Linear Mixed Effects Regression (LMER) models were performed for all the acoustic measures, using the lme4 package (Bates et al. 2015). The lmerTest package was used to obtain p values for all the LMER models with Satterthwaite approximations for degrees of freedom (Kuznetsova et al. 2017). In all the LMER models, Laryngeal (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) and Place (bilabial, dental, retroflex, palatal, and velar) were included as fixed factors, and Speaker as a random factor (alpha value: p = 0.05). By-speaker random slopes for Laryngeal and Place were included in all the models. Pairwise comparisons were performed using the emmeans package (Lenth 2016). The correlation networks were plotted using the Gaussian Graphical Model (Bhushan et al. 2019; Epskamp et al. 2018).

3. Results

Figure 3 presents violin plots of F0. The ranking of the F0 onsets at each place of articulation is shown in Table 1 whereas LMER models are presented in Table 2. F0 onsets reliably differentiated the voiced unaspirated vs. voiceless aspirated stops (p < 0.001) and voiced unaspirated vs. voiceless unaspirated stops (p < 0.001). The average F0 values of each laryngeal category showed the following pattern: voiceless aspirated > voiceless unaspirated = voiced unaspirated. In terms of place, bilabial stops were significantly different from retroflex (p < 0.001) and velar (p = 0.001) stops (see also interactions among place and laryngeal categories in Table 2). There were minor differences in voiceless aspirated and voiceless unaspirated stops. F0 of voiceless aspirated vs. voiceless unaspirated stops is non-significant at all places of articulation, except palatal (see pairwise comparisons in Table A2 in Appendix A). F0 ranking of the three laryngeal categories at each place of articulation (Table 1) suggests that voiceless aspirated stops of Shina are characterized by higher F0 onsets at all the places of articulation, except palatal.
Spectral tilt/balance measures are generally higher (steeper) for breathier phonations and lower (shallower or negative) for creakier phonations (see discussion above). Figure 4 presents violin plots of H1*-H2*, H1*-A1*, H1*-A2*, and H1*-A3*. Table 2 shows results of the LMER models. H1*-H2* differentiated the voiced unaspirated vs. voiceless unaspirated stops (p < 0.001) and bilabials vs. palatals (p = 0.001), bilabials vs. retroflexes (p = 0.002), and bilabials vs. velars (p = 0.002). H1*-H2* onset was higher in the voiceless aspirated than voiceless unaspirated and voiced unaspirated stops (voiceless aspirated > voiceless unaspirated > voiced unaspirated).
H1*-A1* onsets distinguished the voiced unaspirated vs. voiceless aspirated stops (p = 0.001) and voiced unaspirated vs. voiceless unaspirated stops (p < 0.001). In terms of place contrasts, bilabials vs. retroflexes (p = 0.002) and bilabials vs. velars (p = 0.006) showed significant differences in H1*-A1*. Voiceless aspirated stops were characterized by slightly higher H1*-A1* onsets than voiceless unaspirated and voiced unaspirated stops (voiceless aspirated > voiceless unaspirated > voiced unaspirated).
H1*-A2* onsets differentiated the voiced unaspirated vs. voiceless aspirated stops (p = 0.001) and voiced unaspirated vs. voiceless unaspirated stops (p < 0.001). Bilabials vs. palatals (p = 0.045), bilabials vs. retroflexes (p < 0.001), and bilabials vs. velars (p < 0.001) showed significant differences in H1*-A2*. The voiceless aspirated stops exhibited the highest H1*-A2* onsets, followed by voiceless unaspirated and voiced unaspirated stops (voiceless aspirated > voiceless unaspirated > voiced unaspirated).
H1*-A3* onsets distinguished the voiced unaspirated vs. voiceless aspirated stops (p = 0.005) and voiced unaspirated vs. voiceless unaspirated stops (p = 0.001). Bilabials vs. palatals (p = 0.003), bilabials vs. retroflexes (p = 0.003), and bilabials vs. velars (p < 0.001) showed significant differences in H1*-A3*. The general pattern of H1*-A3* across three laryngeal categories was voiceless aspirated > voiceless unaspirated > voiced unaspirated.
Figure 3 presents violin plots of CPP. The LMER model of CPP (Table 2) indicated a significant difference in voiced unaspirated vs. voiceless aspirated stops (p = 0.009) and voiced unaspirated vs. voiceless unaspirated stops (p = 0.007). Bilabials vs. retroflexes (p = 0.008) and bilabials vs. velars (p < 0.001) had significant differences in CPP. There were no clear patterns of CPP across three laryngeal categories of Shina (Figure 3). However, voiceless aspirated stops showed higher CPP onsets than the other two laryngeal categories (voiceless aspirated > voiceless unaspirated > voiced unaspirated).

Correlations

Correlations among F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP were examined using the Gaussian Graphical Model (GGM) (Bhushan et al. 2019; Epskamp et al. 2018). The advantage of GGM correlation networks is their ease of interpretation and visualization. In Figure 5, the thickness and colour shading of the lines indicate the strength of the relationships among acoustic variables. It can be observed that the four spectral tilt measures were highly correlated with F0. Table 3 shows results of the correlation models. Within the category of voiceless unaspirated stops, the highest correlation was found in F0 × H1*-A1* (r = 1.00) and the lowest correlation in H1*-H2* × CPP (r = 0.88). This pattern can also be visualized in the colour and thickness of the lines in Figure 5 (top correlation network: voiceless unaspirated). In the voiceless aspirated stops, the highest correlation was found in H1*-A1* × H1*-A3* (r = 1.00) and the lowest correlation in H1*-H2* × CPP (r = 0.86). Within the category of voiced unaspirated stops, the highest correlations were observed in F0 × H1*-A1* (r = 1.00), F0 × H1*-A2* (r = 1.00), F0 × H1*-A3* (r = 1.00), H1*-A1* × H1*-A2* (r = 1.00), and H1*-A1* × H1*-A3* (r = 1.00) but lowest correlation was found in H1*-H2* × CPP (r = 0.96). All the correlations suggest a strong relationship among the acoustic variables.

4. Discussion

The current study investigated the F0, spectral tilt, and CPP of the three-way laryngeal contrast across five places of articulation in Shina, an endangered Indo-Aryan (Dardic) language spoken in Gilgit, Northern Pakistan. The findings indicated that Shina speakers may rely on a number of acoustic correlates to tease apart the laryngeal and place contrasts. F0 onsets were higher in the voiceless aspirated stops than voiceless unaspirated and voiced unaspirated stops. This suggests that Shina is among those languages where aspiration has a raising effect on the F0 onsets of the following vowels (Chru: Brunelle et al. 2020; Khmer, Central Thai, and Northern Vietnamese: Kirby 2018a; Madurese: Misnadin 2016: Thai: Shimizu 1989) but it differs from other languages where voiceless unaspirated stops raise the F0 onsets (Burushaski: Hussain 2021; Burmese: Shimizu 1989; Cantonese: Luo et al. 2016; Hindi: Shimizu 1989; Kalasha: Hussain and Mielke 2020; Mandarin: Luo et al. 2016; Marathi: Dmitrieva and Dutta 2020; Shanghai Chinese: Luo et al. 2016). Voiceless aspirated stops are produced with a wider opening of the vocal cords so that the air can pass through without any obstruction (Kim et al. 2010). Thus, a wider opening during the aspiration/frication intervals of voiceless aspirated stops leads to the raising of F0 onsets on the following vowels. We also noted significant effects of place on F0 onsets in Shina. It needs to be further investigated how different vocal tract configurations affect F0 onsets (see discussion below).
Voiceless aspirated stops were characterized by the highest H1*-H2*, H1*-A1*, H1*-A2*, and H1*-A3* onsets compared to other two laryngeal categories. These results are in line with other languages of the world where aspirated stops have been shown as having a higher spectral tilt at the onset of the following vowels than unaspirated stops (Shanghai Chinese: Gao et al. 2020; Yerevan Armenian: Seyfarth and Garellek 2018; Dzongkha: Kirby and Hyslop 2019; Hindi/Urdu: Schertz and Khan 2020; Gujarati: Esposito et al. 2020; Marathi: Berkson 2019; Dmitrieva and Dutta 2020). Raising of the spectral tilt due to aspiration appears to be a universal correlate. There are no known languages where we observe that aspiration lowers the spectral tilt. Voiced stops of Shina were consistently characterized by the lowest spectral tilt which also appears to be consistent across languages. The results of CPP were surprising. Breathiness/aspiration causes less distinct harmonics in the acoustic signal which lowers the CPP values (Garellek 2019). However, in Shina’s voiceless aspirated stops, CPP values were higher than voiceless unaspirated and voiced unaspirated stops. These results differ from Madurese (Misnadin 2016) and Yerevan Armenian (Seyfarth and Garellek 2018) which showed low CPP in aspirated stops.
F0, spectral tilt, and CPP measures have been primarily used for investigating the laryngeal contrasts (Kirby 2018a; Kirby and Hyslop 2019; Lee and Kawahara 2018; Seyfarth and Garellek 2018). However, their usage for the characterization of rich place contrasts is not widely explored (Hussain 2021; Misnadin and Kirby 2020). Lai et al. (2009) observed minor but significant effects of place on F0 onsets in Taiwanese. The laryngeal distinctions are mainly achieved by vibrating (opening/closing) of the vocal cords. Thus, all the spectral tilt measures are sensitive to these subtle movements. In contrast, different places of articulation are achieved by making constrictions in the vocal tract (there are some places of articulation that involve constrictions in the pharyngeal cavity, e.g., pharyngeal consonants, glottal stops). It still needs to be explored how different vocal tract configurations (which make the front and back cavities either long or short, e.g., larger sublingual cavity in retroflexes) affect the vocal cord vibrations. In the current study, there appeared to be minor place differences in F0 and spectral tilt. However, at most of the places of articulation, voiceless aspirated stops were characterized by the highest F0 and spectral tilt. This suggests that the F0 and spectral tilt rankings are consistent across laryngeal categories and places of articulation.
Significant correlations were observed among F0 and all the other acoustic measures. These findings corroborate other studies which reported correlations among F0 and spectral tilt (Iseli et al. 2007). Koreman (1996) found that higher F0 and H1*-H2* may occur due to the increasing tension in the cricothyroid muscle in the larynx (see also Kreiman et al. 2012). These correlations may also help inform the emergence of tones (tonogenesis) and phonation (phonationgenesis) contrasts in the world’s languages. A wide range of Sino-Tibetan and Austronesian languages contrast tones on the dimension of phonation (checked, creaky, high, and low tones in Burmese; Gruber 2011). The development of phonation and tonal contrasts in different varieties of Kammu has been explored using comparative lexical data. For example, one Western Kammu variety has low tone (/pùːc/ ‘rice wine’) where another has breathiness (/pṳːc/), and both stem from the voicing still seen in Eastern Kammu (/buːc/) (Kingston 2011). This indicates that the development of tonal contrasts may occur together with the development of phonation contrasts (see discussion in Garellek and Esposito 2021; Gruber 2011; Ratliff 2015).
In a typological review, Esposito and Khan (2020) grouped languages into four categories: (a) languages which do not use phonation as a cue for tone (Japanese, Navajo, Punjabi, Manange, most West African languages, Swedish, and Central Thai); (b) languages which use phonation as a cue for tone (Cantonese Yue, Khmu’ Rawk, Mandarin, Pakphanang Thai, Ph. Penh Khmer, and Yueyang Xiang); (c) languages where phonation and tone are orthogonal (Dinka, Mazatec languages, Mpi, Yalálag Zapotec, and Yi languages); and (d) languages which fuse the phonation and tone (Black Miao, Burmese, Green Mong, most Zapotec languages, Tamang, Vietnamese, White Hmong, and Wu). Thus, in some languages, there may be a strong relationship among F0 and the phonation measures. Shina uses both F0 and phonation to distinguish the three-way laryngeal contrast which, at some point, may also contribute to the development of phonemic tones.
The emergence of tones in a language can be linked to the type of laryngeal categories. The onset-based tonogenesis more often arises from the voiced unaspirated category going its own path, apart from all voiceless categories, or the voiced aspirated category going its own path. The effect of voiced (un)aspirated stops has been widely acknowledged in the tonogenesis literature (Hombert et al. 1979; Ratliff 2015) but frication can also play a key role. For example, glottal fricative /h/ in Punjabi led to the emergence of high tone (Urdu /kehna/ vs. Punjabi /kéna/ ‘say’; Hussain 2020; Hussain et al. 2019). Voiceless aspirated stops involve two acoustic/articulatory events: stop closure + aspiration/frication of /h/. Fricatives, in general, have a raising effect on F0 (Michaud and Sands 2020; Pearce 2007). Moreover, fricative /h/ and the aspiration interval of voiceless aspirated stops share the articulatory feature associated with the vocal cords (e.g., both have a wider opening of the vocal cords). Thus, we can assume that at some point, voiceless aspirated stops will have the same effect on F0 as found in glottal fricative /h/ (i.e., raise the F0 and then lead to the development of high tone). In other words, if frication of /h/ can result in high tone in Punjabi, then the aspiration/frication in voiceless aspirated stops can also clear path for the development of tones. It appears that breathiness caused by the aspiration of the word-initial consonants (as noted in Shina) may be the first step in the development of contrastive tones and/or phonation. Languages may transfer the aspiration feature to the following vowels and may either develop phonemic tones (Punjabi: Hussain et al. 2019), breathy vowels (Gujarati: Esposito and Khan 2012), or both (Western Kammu varieties: Kingston 2011). Dardic languages perhaps belong to the type (a) languages discussed by Esposito and Khan (2020). As phonetic data of Dardic languages are still scarce, a cross-linguistic acoustic/articulatory study is needed to further investigate the relationships among F0, phonation, and width of the vocal cords during the production of various laryngeal categories and places of articulation. Acoustic-phonetic data would also be needed to investigate tonogenesis and phonationgenesis in Shina and neighbouring Dardic languages.
The findings of this study will contribute to the phonetic documentation of endangered Dardic languages. The study also has some limitations. Acoustic data were collected from five male speakers of Shina. Future studies with a larger sample of speakers (both males and females), different age groups (younger, middle-aged, and older), and dialects of Shina (Astor, Gilgit, and Gurez) can complement the findings of the current study. Articulatory experiments using electroglottography (EGG) can also help inform the correlations among various acoustic and articulatory measures.

Funding

The study was funded by a Faculty of Arts and Science Postdoctoral Fellowship Award (University of Toronto, Canada) and a Documenting Endangered Languages grant (BCS-1562134) from the National Science Foundation, USA, and NCSU Department of English.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the IRB of the North Carolina State University, Raleigh (Protocol Number 7745).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A

Table
Table A1. List of nonsense words of Shina. The target stops occurred in word-initial position, followed by a neutral vowel /a/.
Table A1. List of nonsense words of Shina. The target stops occurred in word-initial position, followed by a neutral vowel /a/.
PlaceLaryngealIPA
Voiceless Unaspirated/pa/
BilabialVoiceless Aspirated/pha/
Voiced Unaspirated/ba/
Voiceless Unaspirated/t̪a/
DentalVoiceless Aspirated/t̪ha/
Voiced Unaspirated/d̪a/
Voiceless Unaspirated/ʈa/
RetroflexVoiceless Aspirated/ʈha/
Voiced Unaspirated/ɖa/
Voiceless Unaspirated/ʧa/
PalatalVoiceless Aspirated/ʧha/
Voiced Unaspirated/ʤa/
Voiceless Unaspirated/ka/
VelarVoiceless Aspirated/kha/
Voiced Unaspirated/ɡa/
Table
Table A2. Pairwise comparisons: three laryngeal categories are compared across five places of articulation.
Table A2. Pairwise comparisons: three laryngeal categories are compared across five places of articulation.
F0H1*-H2*
PlaceComparisonsEst.tpEst.tp
BilabialVoiced Unaspirated vs. Voiceless Aspirated−6.83−8.45<0.001−1.90−3.64=0.033
Voiced Unaspirated vs. Voiceless Unaspirated−5.96−10.03<0.001−1.72−5.02=0.004
Voiceless Aspirated vs. Voiceless Unaspirated0.871.76=0.2270.180.63=0.805
DentalVoiced Unaspirated vs. Voiceless Aspirated−10.14−12.76<0.001−3.18−6.10=0.004
Voiced Unaspirated vs. Voiceless Unaspirated−9.00−15.64<0.001−2.27−6.63=0.001
Voiceless Aspirated vs. Voiceless Unaspirated1.142.31=0.0970.923.22=0.023
RetroflexVoiced Unaspirated vs. Voiceless Aspirated−1.60−2.01=0.194−0.66−1.26=0.473
Voiced Unaspirated vs. Voiceless Unaspirated−0.75−1.30=0.429−0.33−0.93=0.640
Voiceless Aspirated vs. Voiceless Unaspirated0.841.70=0.2500.341.16=0.502
PalatalVoiced Unaspirated vs. Voiceless Aspirated2.553.08=0.0430.761.42=0.393
Voiced Unaspirated vs. Voiceless Unaspirated4.467.14<0.0010.992.72=0.057
Voiceless Aspirated vs. Voiceless Unaspirated1.913.81=0.0070.220.79=0.720
VelarVoiced Unaspirated vs. Voiceless Aspirated−2.61−3.27=0.041−0.48−0.92=0.653
Voiced Unaspirated vs. Voiceless Unaspirated−1.55−2.69=0.058−0.29−0.84=0.691
Voiceless Aspirated vs. Voiceless Unaspirated1.062.13=0.1290.190.67=0.784
H1*-A1*H1*-A2*
PlaceComparisonsEst.tpEst.tp
BilabialVoiced Unaspirated vs. Voiceless Aspirated−1.11−5.62=0.002−1.87−4.53=0.009
Voiced Unaspirated vs. Voiceless Unaspirated−0.96−7.57<0.001−1.67−6.03<0.001
Voiceless Aspirated vs. Voiceless Unaspirated0.151.07=0.5510.190.74=0.743
DentalVoiced Unaspirated vs. Voiceless Aspirated−2.08−10.76<0.001−3.07−7.54=0.001
Voiced Unaspirated vs. Voiceless Unaspirated−1.60−13.25<0.001−2.18−8.06<0.001
Voiceless Aspirated vs. Voiceless Unaspirated0.483.42=0.0140.893.45=0.012
RetroflexVoiced Unaspirated vs. Voiceless Aspirated−0.32−1.66=0.292−0.49−1.19=0.501
Voiced Unaspirated vs. Voiceless Unaspirated−0.15−1.22=0.454−0.21−0.78=0.723
Voiceless Aspirated vs. Voiceless Unaspirated0.171.21=0.4700.271.05=0.559
PalatalVoiced Unaspirated vs. Voiceless Aspirated0.391.92=0.1980.501.17=0.506
Voiced Unaspirated vs. Voiceless Unaspirated0.825.99<0.0010.872.90=0.029
Voiceless Aspirated vs. Voiceless Unaspirated0.433.02=0.0270.371.41=0.363
VelarVoiced Unaspirated vs. Voiceless Aspirated−0.40−2.08=0.171−0.51−1.26=0.466
Voiced Unaspirated vs. Voiceless Unaspirated−0.25−2.01=0.136−0.32−1.16=0.504
Voiceless Aspirated vs. Voiceless Unaspirated0.161.12=0.5210.200.78=0.724
H1*-A3*CPP
PlaceComparisonsEst.tpEst.tp
BilabialVoiced Unaspirated vs. Voiceless Aspirated−1.87−4.53=0.009−2.77−4.46=0.020
Voiced Unaspirated vs. Voiceless Unaspirated−1.67−6.03<0.001−2.50−4.72=0.015
Voiceless Aspirated vs. Voiceless Unaspirated0.190.74=0.7430.271.53=0.298
DentalVoiced Unaspirated vs. Voiceless Aspirated−3.07−7.54=0.001−3.16−5.09=0.012
Voiced Unaspirated vs. Voiceless Unaspirated−2.18−8.06<0.001−2.77−5.25=0.010
Voiceless Aspirated vs. Voiceless Unaspirated0.893.45=0.0120.392.16=0.102
RetroflexVoiced Unaspirated vs. Voiceless Aspirated−0.49−1.19=0.501−0.55−0.89=0.675
Voiced Unaspirated vs. Voiceless Unaspirated−0.21−0.78=0.723−0.28−0.53=0.860
Voiceless Aspirated vs. Voiceless Unaspirated0.271.05=0.5590.271.48=0.319
PalatalVoiced Unaspirated vs. Voiceless Aspirated0.501.17=0.5060.160.25=0.966
Voiced Unaspirated vs. Voiceless Unaspirated0.872.90=0.0290.470.88=0.674
Voiceless Aspirated vs. Voiceless Unaspirated0.371.41=0.3630.321.76=0.207
VelarVoiced Unaspirated vs. Voiceless Aspirated−0.51−1.26=0.466−0.72−1.17=0.526
Voiced Unaspirated vs. Voiceless Unaspirated−0.32−1.16=0.504−0.39−0.75=0.751
Voiceless Aspirated vs. Voiceless Unaspirated0.200.78=0.7240.331.84=0.182

Notes

1
Strand (2001a) also listed several words with aspirated stops. More research is needed to investigate the number of laryngeal contrasts in Bateri.
2
Kashmiri also contrasts palatalized stops in addition to plain stops (Koul 2003; Wali and Koul 1997).
3
These two languages are shown here because of their relevance for the three laryngeal categories of Shina.

References

  1. Baart, Joan L. G. 1999. Tone rules in Kalam Kohistani (Garwi, Bashkarik). Bulletin of the School of Oriental and African Studies 62: 88–104. [Google Scholar] [CrossRef]
  2. Backstrom, Peter C., and Carla F. Radloff. 1992. Sociolinguistic Survey of Northern Pakistan (Volume 2: Languages of Northern Areas). Islamabad: National Institute of Pakistan Studies and Summer Institute of Linguistics. [Google Scholar]
  3. Bashir, Elena. 2003. Dardic. In The Indo-Aryan Languages. Edited by George Cardona and Dhanesh Jain. London: Routledge, pp. 818–94. [Google Scholar]
  4. Bates, Douglas, Martin Mächler, Ben Bolker, and Steve Walker. 2015. Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67: 1–48. [Google Scholar] [CrossRef]
  5. Berkson, Kelly Harper. 2019. Acoustic correlates of breathy sonorants in Marathi. Journal of Phonetics 73: 70–90. [Google Scholar] [CrossRef]
  6. Bhushan, Nitin, Florian Mohnert, Daniel Sloot, Lise Jans, Casper Albers, and Linda Steg. 2019. Using a Gaussian Graphical Model to explore relationships between items and variables in environmental psychology research. Frontiers in Psychology 10: 1050. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Boersma, Paul, and David Weenink. 2007. Praat: Doing Phonetics by Computer [Computer Program]. Version 6.0.30. Available online: http://www.praat.org (accessed on 30 July 2017).
  8. Brunelle, Marc, Ta Thanh Tan, James Kirby, and Dinh Lu Giang. 2019. Obstruent devoicing and registrogenesis in Chru. In Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019. Edited by Sasha Calhoun, Paola Escudero, Marija Tabain and Paul Warren. Canberra: Australasian Speech Science and Technology Association Inc., pp. 517–21. [Google Scholar]
  9. Brunelle, Marc, Ta Thanh Tan, James Kirby, and Dinh Lu Giang. 2020. Transphonologization of voicing in Chru: Studies in production and perception. Laboratory Phonology 11: 1–33. [Google Scholar] [CrossRef]
  10. Cacopardo, Alberto M., and Augusto S. Cacopardo. 2001. Gates of Peristan: History, Religion and Society in the Hindu Kush. Rome: Instituto Italiano Per L’ Africa E L’Oriente, Centro Scavi E Ricerche Archeologiche, vol. 5. [Google Scholar]
  11. Chen, Yiya. 2011. How does phonology guide phonetics in segment–f0 interaction? Journal of Phonetics 39: 612–25. [Google Scholar] [CrossRef]
  12. Decker, Sandra J. 1992. Ushojo. In Sociolinguistic Survey of Northern Pakistan (Volume 1: Languages of Kohistan). Edited by Calvin R. Rensch, Sandra J. Decker and Daniel G. Hallberg. Islamabad: National Institute of Pakistan Studies and Summer Institute of Linguistics, pp. 65–82. [Google Scholar]
  13. Dmitrieva, Olga, and Indranil Dutta. 2020. Acoustic correlates of the four-way laryngeal contrast in Marathi. Phonetica 77: 209–37. [Google Scholar] [CrossRef] [PubMed]
  14. Epskamp, Sacha, Denny Borsboom, and Eiko I. Fried. 2018. Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods 50: 195–212. [Google Scholar] [CrossRef] [Green Version]
  15. Esposito, Christina M., and Sameer Dowla Khan. 2012. Contrastive breathiness across consonants and vowels: A comparative study of Gujarati and White Hmong. Journal of the International Phonetic Association 42: 123–43. [Google Scholar] [CrossRef] [Green Version]
  16. Esposito, Christina M., and Sameer Dowla Khan. 2020. The cross-linguistic patterns of phonation types. Language and Linguistics Compass 14: e12392. [Google Scholar] [CrossRef]
  17. Esposito, Christina M., Sameer Dowla Khan, Kelly H. Berkson, and Max Nelson. 2020. Distinguishing breathy consonants and vowels in Gujarati. Journal of South Asian Languages and Linguistics 6: 215–43. [Google Scholar] [CrossRef]
  18. Gao, Jiayin, Pierre Hallé, and Christoph Draxler. 2020. Breathy voice and low-register: A case of trading relation in Shanghai Chinese tone perception? Language and Speech 63: 582–607. [Google Scholar] [CrossRef] [PubMed]
  19. Garellek, Marc. 2019. The phonetics of voice. In The Routledge Handbook of Phonetics. Edited by William F. Katz and Peter F. Assmann. Bloomington: Routledge, pp. 75–106. [Google Scholar]
  20. Garellek, Marc, and Christina M. Esposito. 2021. Phonetics of White Hmong vowel and tonal contrasts. Journal of the International Phonetic Association 1–20. [Google Scholar] [CrossRef]
  21. Garellek, Marc, and Patricia Keating. 2011. The acoustic consequences of phonation and tone interactions in Jalapa Mazatec. Journal of the International Phonetic Association 41: 185–205. [Google Scholar] [CrossRef] [Green Version]
  22. Gruber, James Frederick. 2011. An Articulatory, Acoustic, and Auditory Study of Burmese Tone. Ph.D. thesis, Georgetown University, Washington, DC, USA. [Google Scholar]
  23. Hombert, Jean-Marie, John J. Ohala, and William G. Ewan. 1979. Phonetic explanations for the development of tones. Language 55: 37–58. [Google Scholar] [CrossRef]
  24. House, Arthur S., and Grant Fairbanks. 1953. The influence of consonant environment upon the secondary acoustical characteristics of vowels. Journal of the Acoustical Society of America 25: 105–13. [Google Scholar] [CrossRef]
  25. Hussain, Qandeel. 2018. A typological study of Voice Onset Time (VOT) in Indo-Iranian languages. Journal of Phonetics 71: 284–305. [Google Scholar] [CrossRef]
  26. Hussain, Qandeel. 2020. Punjabi (India and Pakistan)—Language Snapshot. Language Documentation and Description 19: 144–53. [Google Scholar]
  27. Hussain, Qandeel. 2021. Phonetic correlates of laryngeal and place contrasts of Burushaski. Speech Communication 126: 71–89. [Google Scholar] [CrossRef]
  28. Hussain, Qandeel, and Jeff Mielke. 2020. An acoustic and articulatory study of laryngeal and place contrasts of Kalasha (Indo-Aryan, Dardic). Journal of the Acoustical Society of America 147: 2873–90. [Google Scholar] [CrossRef] [PubMed]
  29. Hussain, Qandeel, Michael Proctor, Mark Harvey, and Katherine Demuth. 2019. Punjabi (Lyallpuri variety). Journal of the International Phonetic Association 50: 282–97. [Google Scholar] [CrossRef]
  30. Iseli, Markus, Yen-Liang Shue, and Abeer Alwan. 2007. Age, sex, and vowel dependencies of acoustic measures related to the voice source. Journal of the Acoustical Society of America 121: 2283–95. [Google Scholar] [CrossRef] [Green Version]
  31. Keating, Patricia, and Christina Esposito. 2007. Linguistic voice quality. UCLA Working Papers in Phonetics 105: 85–91. [Google Scholar]
  32. Kim, Hyunsoon, Shinji Maeda, and Kiyoshi Honda. 2010. Invariant articulatory bases of the features [tense] and [spread glottis] in Korean plosives: New stroboscopic cine-MRI data. Journal of Phonetics 38: 90–108. [Google Scholar] [CrossRef]
  33. Kingston, John. 2011. Tonogenesis. In The Blackwell Companion to Phonology. Edited by Marc van Oostendorp, Colin J. Ewen, Elizabeth Hume and Keren Rice. Chichester: Wiley Online Library, pp. 2304–34. [Google Scholar]
  34. Kirby, James P. 2018a. Onset pitch perturbations and the cross-linguistic implementation of voicing: Evidence from tonal and non-tonal languages. Journal of Phonetics 71: 326–54. [Google Scholar] [CrossRef]
  35. Kirby, James P. 2018b. PraatSauce: Praat-Based Tools for Spectral Analysis, Version 0.2.4; Computer Program; Available online: https://github.com/kirbyj/praatsauce (accessed on 8 April 2020).
  36. Kirby, James P., and Gwendolyn Hyslop. 2019. Acoustic analysis of onset voicing in Dzongkha obstruents. In Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019. Edited by Sasha Calhoun, Paola Escudero, Marija Tabain and Paul Warren. Canberra: Australasian Speech Science and Technology Association Inc., pp. 3607–11. [Google Scholar]
  37. Kirby, James P., and D. Robert Ladd. 2016. Effects of obstruent voicing on vowel F0: Evidence from “true voicing” languages. Journal of the Acoustical Society of America 140: 2400–11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Kochetov, Alexei, Paul Arsenault, Jan Heegård Petersen, Sikandar Kalas, and Taj Khan Kalash. 2020. Kalasha (Bumburet variety). Journal of the International Phonetic Association, 1–22. [Google Scholar] [CrossRef]
  39. Koreman, Jacobus Johannes Maria. 1996. Decoding Linguistic Information in the Glottal Airflow. Ph.D. thesis, University of Nijmegen, Nijmegen, The Netherlands. [Google Scholar]
  40. Koul, Omkar N. 2003. Kashmiri. In The Indo-Aryan Languages. Edited by George Cardona and Dhanesh Jain. London: Routledge, pp. 991–1051. [Google Scholar]
  41. Kreiman, Jody, Yen-Liang Shue, Gang Chen, Markus Iseli, Bruce R. Gerratt, Juergen Neubauer, and Abeer Alwan. 2012. Variability in the relationships among voice quality, harmonic amplitudes, open quotient, and glottal area waveform shape in sustained phonation. Journal of the Acoustical Society of America 132: 2625–32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Kuznetsova, Alexandra, Per B. Brockhoff, and Rune Haubo Bojesen Christensen. 2017. lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software 82: 1–26. [Google Scholar] [CrossRef] [Green Version]
  43. Ladd, D. Robert, and Stephan Schmid. 2018. Obstruent voicing effects on F0, but without voicing: Phonetic correlates of Swiss German lenis, fortis, and aspirated stops. Journal of Phonetics 71: 229–48. [Google Scholar] [CrossRef]
  44. Lai, Yuwen, Christopher Huff, Joan Sereno, and Allard Jongman. 2009. The raising effect of aspirated prevocalic consonants on F0 in Taiwanese. Paper presented at the 2nd International Conference on East Asian Linguistics, Simon Fraser University Working Papers in Linguistics; Available online: https://www.sfu.ca/content/dam/sfu/linguistics/Gradlings/SFUWPL/Lai_EtAl.pdf (accessed on 8 April 2020).
  45. Lee, Seunghun, and Shigeto Kawahara. 2018. The phonetic structure of Dzongkha: A preliminary study. Journal of the Phonetic Society of Japan 22: 13–20. [Google Scholar]
  46. Lee, Seunghun J., Shigeto Kawahara, Céleste Guillemot, and Tomoko Monou. 2019. Acoustics of the four-way laryngeal contrast in Drenjongke (Bhutia): Observations and implications. Journal of the Phonetic Society of Japan 23: 65–75. [Google Scholar]
  47. Lenth, Russell V. 2016. Least-Squares Means: The R package lsmeans. Journal of Statistical Software 69: 1–33. [Google Scholar] [CrossRef] [Green Version]
  48. Liljegren, Henrik. 2013. Notes on Kalkoti: A Shina language with strong Kohistani influences. Linguistic Discovery 11: 129–60. [Google Scholar] [CrossRef] [Green Version]
  49. Liljegren, Henrik. 2017. Profiling Indo-Aryan in the Hindukush-Karakoram: A preliminary study of micro-typological patterns. Journal of South Asian Languages and Linguistics 4: 107–56. [Google Scholar] [CrossRef]
  50. Liljegren, Henrik, and Fakhruddin Akhunzada. 2017. Linguistic diversity, vitality and maintenance: A case study on the language situation in northern Pakistan. Multiethnica, 61–79. [Google Scholar]
  51. Liljegren, Henrik, and Naseem Haider. 2009. Palula. Journal of the International Phonetic Association 39: 381–86. [Google Scholar] [CrossRef] [Green Version]
  52. Liljegren, Henrik, and Afsar Ali Khan. 2017. Khowar. Journal of the International Phonetic Association 47: 219–29. [Google Scholar] [CrossRef] [Green Version]
  53. Luo, Qian, Karthik Durvasula, and Yen-Hwei Lin. 2016. Inconsistent consonantal effects on f0 in Cantonese and Mandarin. Paper presented at the Tonal Aspects of Languages, Buffalo, NY, USA, May 24–27; pp. 52–55. [Google Scholar]
  54. Michaud, Alexis, and Bonny Sands. 2020. Tonogenesis. In Oxford Research Encyclopedia of Linguistics. Oxford: Oxford University Press. [Google Scholar] [CrossRef]
  55. Misnadin. 2016. Phonetics and Phonology of the Three-way Laryngeal Contrast in Madurese. Ph.D. thesis, The University of Edinburgh, Edinburgh, UK. [Google Scholar]
  56. Misnadin, and James Kirby. 2020. Acoustic correlates of plosive voicing in Madurese. Journal of the Acoustical Society of America 147: 2779–90. [Google Scholar] [CrossRef]
  57. Morgenstierne, Georg. 1932. Report on a Linguistic Mission to North-Western India. Norway: Det Mallingske Bogtrykkeri. [Google Scholar]
  58. Morgenstierne, Georg. 1940. Notes on Bashkarik. Acta Orientalia 18, Pt 3: 4. [Google Scholar]
  59. Morgenstierne, Georg. 1942. Notes on Dameli: A Kafir-Dardic language of Chitral. Norsk Tidsskrift for Sprogvidenskap 12: 115–98. [Google Scholar]
  60. Ohde, Ralph N. 1984. Fundamental frequency as an acoustic correlate of stop consonant voicing. Journal of the Acoustical Society of America 75: 224–30. [Google Scholar] [CrossRef]
  61. Pearce, Mary Dorothy. 2007. The Interaction of Tone with Voicing and Foot Structure: Evidence from Kera Phonetics and Phonology. Ph.D. thesis, University College London, London, UK. [Google Scholar]
  62. Perder, Emil. 2013. A Grammatical Description of Dameli. Ph.D. thesis, Stockholm University, Stockholm, Sweden. [Google Scholar]
  63. R Core Team. 2013. R: A Language and Environment for Statistical Computing. Version 3.6.3; Computer Program. Vienna: R Foundation for Statistical Computing. [Google Scholar]
  64. Radloff, Carla F. 1999. Aspects of the Sound System of Gilgiti Shina. Islamabad: National Institute of Pakistan Studies, Quaid-i-Azam University. [Google Scholar]
  65. Ratliff, Martha. 2015. Tonoexodus, tonogenesis, and tone change. In The Oxford Handbook of Historical Phonology. Edited by Patrick Honeybone and Joseph Salmons. Oxford: Oxford University Press, pp. 245–61. [Google Scholar]
  66. Rehman, Khawaja A., and Joan L. G. Baart. 2005. A first look at the language of Kundal Shahi in Azad Kashmir. SIL Electronic Working Papers 53: 1–22. [Google Scholar]
  67. Schertz, Jessamyn, and Sarah Khan. 2020. Acoustic cues in production and perception of the four-way stop laryngeal contrast in Hindi and Urdu. Journal of Phonetics 81: 1–25. [Google Scholar] [CrossRef]
  68. Seyfarth, Scott, and Marc Garellek. 2018. Plosive voicing acoustics and voice quality in Yerevan Armenian. Journal of Phonetics 71: 425–50. [Google Scholar] [CrossRef]
  69. Shimizu, Katsumasa. 1989. A cross-language study of voicing contrasts of stops. Studia Phonologica 23: 1–12. [Google Scholar]
  70. Strand, Richard F. 1973. Notes on the Nūristāni and Dardic languages. Journal of the American Oriental Society 93: 297–305. [Google Scholar] [CrossRef]
  71. Strand, Richard F. 2001a. The Sound System of Bhat’e-sa z’ib. Available online: https://nuristan.info/ (accessed on 14 June 2021).
  72. Strand, Richard F. 2001b. Tongues of Peristan. In Gates of Peristan: History, Religion, and Society in the Hindu Kush. Edited by Alberto M. Cacopardo and Augusto M. Cacopardo. Rome: Instituto Italiano Per L’ Africa E L’Oriente, Centro Scavi E Ricerche Archeologiche, pp. 251–57. [Google Scholar]
  73. Tan, Ta Thanh, Marc Brunelle, and Nguyen Tran Quy. 2019. Chrau register and the transphonologization of voicing. In Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019. Edited by Sasha Calhoun, Paola Escudero, Marija Tabain and Paul Warren. Canberra: Australasian Speech Science and Technology Association Inc., pp. 2094–98. [Google Scholar]
  74. Wali, Kashi, and Omkar N. Koul. 1997. Kashmiri: A Cognitive-Descriptive Grammar. London: Routledge. [Google Scholar]
  75. Wayland, Ratree P., and Allard Jongman. 2002. Registrogenesis in Khmer: A phonetic account. Mon-Khmer Studies 32: 101–15. [Google Scholar]
Languages 06 00139 g001 550
Figure 1. Map of Northwestern region of South Asia where Dardic languages are spoken. Number of laryngeal contrasts (2-, 3-, or 4-way) found in Dardic languages are shown.
Figure 1. Map of Northwestern region of South Asia where Dardic languages are spoken. Number of laryngeal contrasts (2-, 3-, or 4-way) found in Dardic languages are shown.
Languages 06 00139 g001
Languages 06 00139 g002 550
Figure 2. F0 onsets across three laryngeal categories of Burmese and Thai (average values based on Shimizu 1989, p. 5).
Figure 2. F0 onsets across three laryngeal categories of Burmese and Thai (average values based on Shimizu 1989, p. 5).
Languages 06 00139 g002
Languages 06 00139 g003 550
Figure 3. Violin plots of F0 and CPP. Black lines connect the means of the three laryngeal categories (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) at each place of articulation (bilabial, dental, retroflex, palatal, and velar).
Figure 3. Violin plots of F0 and CPP. Black lines connect the means of the three laryngeal categories (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) at each place of articulation (bilabial, dental, retroflex, palatal, and velar).
Languages 06 00139 g003
Languages 06 00139 g004 550
Figure 4. Violin plots of H1*-H2*, H1*-A1* H1*-A2*, and H1*-A3*. Black lines connect the means of the three laryngeal categories (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) at each place of articulation (bilabial, dental, retroflex, palatal, and velar).
Figure 4. Violin plots of H1*-H2*, H1*-A1* H1*-A2*, and H1*-A3*. Black lines connect the means of the three laryngeal categories (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) at each place of articulation (bilabial, dental, retroflex, palatal, and velar).
Languages 06 00139 g004
Languages 06 00139 g005 550
Figure 5. Correlation networks of F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP (collapsed across places of articulation). The thickness and colour shading of lines represent the strength of the relationships among all the acoustic variables. The absence of a line indicates very weak and/or no correlations among the variables.
Figure 5. Correlation networks of F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP (collapsed across places of articulation). The thickness and colour shading of lines represent the strength of the relationships among all the acoustic variables. The absence of a line indicates very weak and/or no correlations among the variables.
Languages 06 00139 g005
Table
Table 1. F0 ranking of the three laryngeal categories at each place of articulation.
Table 1. F0 ranking of the three laryngeal categories at each place of articulation.
PlaceF0 Ranking
Bilabialvoiceless aspirated > voiceless unaspirated > voiced unaspirated
Dentalvoiceless aspirated > voiceless unaspirated > voiced unaspirated
Retroflexvoiceless aspirated > voiceless unaspirated > voiced unaspirated
Palatalvoiced unaspirated > voiceless aspirated > voiceless unaspirated
Velarvoiceless aspirated > voiceless unaspirated > voiced unaspirated
Table
Table 2. Results of LMER models of F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP. Statistically significant results are shown in bold, with an alpha value of p = 0.05.
Table 2. Results of LMER models of F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP. Statistically significant results are shown in bold, with an alpha value of p = 0.05.
F0H1*-H2*
FactorsEst.SEdftpEst.SEdftp
(Intercept)148.307.753.2619.13<0.0011.621.443.921.12=0.325
Laryngeal [Voiceless Aspirated]6.830.816.168.45<0.0011.900.525.033.64=0.015
Laryngeal [Voiceless Unaspirated]5.960.5910.2810.03<0.0011.720.347.105.02=0.001
Place [Dental]1.250.775.701.62=0.1580.870.764.361.13=0.315
Place [Palatal]1.763.013.330.58=0.5971.440.3011.244.73=0.001
Place [Retroflex]7.660.697.0311.13<0.0012.030.405.975.09=0.002
Place [Velar]2.470.529.564.78=0.0011.050.2511.304.15=0.002
Laryngeal [Voiceless Aspirated]*Place [Dental]3.310.53328.176.22<0.0011.280.27330.714.75<0.001
Laryngeal [Voiceless Unaspirated]*Place [Dental]3.040.53328.175.71<0.0010.550.27330.712.03=0.043
Laryngeal [Voiceless Aspirated]*Place [Palatal]−9.380.58330.91−16.16<0.001−2.660.30326.52−8.98<0.001
Laryngeal [Voiceless Unaspirated]*Place [Palatal]−10.420.58330.67−17.87<0.001−2.700.30326.52−9.13<0.001
Laryngeal [Voiceless Aspirated]*Place [Retroflex]−5.230.53328.17−9.83<0.001−1.240.28330.67−4.47<0.001
Laryngeal [Voiceless Unaspirated]*Place [Retroflex]−5.210.54328.05−9.73<0.001−1.390.28330.83−4.95<0.001
Laryngeal [Voiceless Aspirated]*Place [Velar]−4.220.54328.26−7.89<0.001−1.420.27330.72−5.22<0.001
Laryngeal [Voiceless Unaspirated]*Place [Velar]−4.410.54328.26−8.23<0.001−1.430.27330.72−5.26<0.001
H1*-A1*H1*-A2*
FactorsEst.SEdftpEst.SEdftp
(Intercept)12.001.254.089.63=0.00110.031.653.616.06=0.005
Laryngeal [Voiceless Aspirated]1.110.206.805.62=0.0011.870.416.154.53=0.004
Laryngeal [Voiceless Unaspirated]0.960.1321.947.57<0.0011.670.2810.746.03<0.001
Place [Dental]0.200.274.780.74=0.4970.730.594.881.25=0.267
Place [Palatal]0.260.524.860.50=0.6360.950.386.322.49=0.045
Place [Retroflex]1.270.1411.398.89<0.0012.060.326.766.35<0.001
Place [Velar]0.400.1317.723.15=0.0060.980.2223.054.47<0.001
Laryngeal [Voiceless Aspirated]*Place [Dental]0.970.14328.716.72<0.0011.200.27330.814.51<0.001
Laryngeal [Voiceless Unaspirated]*Place [Dental]0.640.14328.714.43<0.0010.510.27330.811.91=0.057
Laryngeal [Voiceless Aspirated]*Place [Palatal]−1.500.16329.99−9.51<0.001−2.360.29330.02−8.14<0.001
Laryngeal [Voiceless Unaspirated]*Place [Palatal]−1.780.16329.90−11.23<0.001−2.540.29330.92−8.68<0.001
Laryngeal [Voiceless Aspirated]*Place [Retroflex]−0.790.14328.71−5.45<0.001−1.380.27330.81−5.18<0.001
Laryngeal [Voiceless Unaspirated]*Place [Retroflex]−0.810.15328.73−5.57<0.001−1.460.27330.85−5.46<0.001
Laryngeal [Voiceless Aspirated]*Place [Velar]−0.710.15328.67−4.86<0.001−1.350.27330.83−5.04<0.001
Laryngeal [Voiceless Unaspirated]*Place [Velar]−0.710.15328.67−4.91<0.001−1.360.27330.83−5.07<0.001
H1*-A3*CPP
FactorsEst.SEdftpEst.SEdftp
(Intercept)−0.143.433.97−0.04=0.96920.432.694.177.59=0.001
Laryngeal [Voiceless Aspirated]3.580.734.824.94=0.0052.770.624.414.46=0.009
Laryngeal [Voiceless Unaspirated]3.210.545.825.93=0.0012.500.534.594.72=0.007
Place [Dental]1.620.914.301.78=0.1441.140.494.482.30=0.076
Place [Palatal]1.950.487.984.12=0.0031.000.514.861.97=0.108
Place [Retroflex]4.000.724.815.51=0.0033.080.664.334.66=0.008
Place [Velar]2.040.3213.506.31<0.0011.430.1642.188.95<0.001
Laryngeal [Voiceless Aspirated]*Place [Dental]1.700.36330.754.75<0.0010.390.21336.781.87=0.063
Laryngeal [Voiceless Unaspirated]*Place [Dental]0.670.36330.751.87=0.0620.280.21336.781.33=0.185
Laryngeal [Voiceless Aspirated]*Place [Palatal]−4.320.39329.54−11.00<0.001−2.930.23338.26−12.75<0.001
Laryngeal [Voiceless Unaspirated]*Place [Palatal]−4.460.39329.54−11.33<0.001−2.970.23338.26−12.93<0.001
Laryngeal [Voiceless Aspirated]*Place [Retroflex]−2.690.36330.75−7.52<0.001−2.220.21336.78−10.60<0.001
Laryngeal [Voiceless Unaspirated]*Place [Retroflex]−2.790.36330.84−7.77<0.001−2.210.21336.88−10.51<0.001
Laryngeal [Voiceless Aspirated]*Place [Velar]−2.800.36330.84−7.69<0.001−2.050.21336.82−9.72<0.001
Laryngeal [Voiceless Unaspirated]*Place [Velar]−2.740.37330.96−7.41<0.001−2.100.21336.82−9.98<0.001
Table
Table 3. Correlations among F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP.
Table 3. Correlations among F0, H1*-H2*, H1*-A1*, H1*-A2*, H1*-A3*, and CPP.
Voiceless UnaspiratedVoiceless AspiratedVoiced Unaspirated
Parameter 1Parameter 2rprprp
F0H1*-A1*1.00<0.0010.99<0.0011.00<0.001
F0H1*-A2*0.99<0.0010.96<0.0011.00<0.001
F0H1*-A3*0.99<0.0010.98<0.0011.00<0.001
F0H1*-H2*0.94<0.0010.92<0.0010.96<0.001
F0CPP0.98<0.0010.98<0.0010.98<0.001
H1*-A1*H1*-A2*0.99<0.0010.99<0.0011.00<0.001
H1*-A1*H1*-A3*0.99<0.0011.00<0.0011.00<0.001
H1*-A1*H1*-H2*0.92<0.0010.94<0.0010.95<0.001
H1*-A1*CPP0.99<0.0010.98<0.0010.98<0.001
H1*-A2*H1*-A3*0.99<0.0010.99<0.0010.99<0.001
H1*-A2*H1*-H2*0.96<0.0010.97<0.0010.96<0.001
H1*-A2*CPP0.97<0.0010.94<0.0010.98<0.001
H1*-A3*H1*-H2*0.94<0.0010.95<0.0010.94<0.001
H1*-A3*CPP0.99<0.0010.97<0.0010.99<0.001
H1*-H2*CPP0.88<0.0010.86<0.0010.90<0.001
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hussain, Q. Fundamental Frequency and Phonation Differences in the Production of Stop Laryngeal Contrasts of Endangered Shina. Languages 2021, 6, 139. https://doi.org/10.3390/languages6030139

AMA Style

Hussain Q. Fundamental Frequency and Phonation Differences in the Production of Stop Laryngeal Contrasts of Endangered Shina. Languages. 2021; 6(3):139. https://doi.org/10.3390/languages6030139

Chicago/Turabian Style

Hussain, Qandeel. 2021. "Fundamental Frequency and Phonation Differences in the Production of Stop Laryngeal Contrasts of Endangered Shina" Languages 6, no. 3: 139. https://doi.org/10.3390/languages6030139

APA Style

Hussain, Q. (2021). Fundamental Frequency and Phonation Differences in the Production of Stop Laryngeal Contrasts of Endangered Shina. Languages, 6(3), 139. https://doi.org/10.3390/languages6030139

Article Metrics

Back to TopTop