Search Results (367)

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article

This perspective paper presents a first-person account of life with motor neurone disease (MND). Through the lens of lived experience, it explores the complex and often prolonged diagnostic journey, shaped in part by the protective grip of denial. This paper then delves into the emotional impact of MND on the individual and their close relationships, capturing the strain on identity and family dynamics. It also highlights the vital role of the multidisciplinary team in providing support throughout the journey. A central focus of the paper is the personal journey of voice banking. It reflects on the restorative experience of reclaiming a pre-disease voice through tools such as ElevenLabs^TM. This narrative underscores the critical importance of early intervention and timely access to voice banking, positioning voice not only as a tool for communication but also as a powerful anchor of identity, dignity, and agency. The paper concludes by highlighting key systemic gaps in MND care. It calls for earlier referral to speech pathology, earlier access to voice banking, access to psychological support from the time of diagnosis, and better integration between research and clinical care. Full article

Toponyms are fundamental geographical resources characterized by their spatial attributes, distinct from general nouns. While natural language provides rich toponymic data beyond traditional surveying methods, its qualitative ambiguity and inherent uncertainty challenge systematic extraction. Traditional toponym recognition methods based on part-of-speech tagging only focus on the surface-level features of words, failing to effectively handle complex scenarios such as alias nesting, metonymy ambiguity, and mixed punctuation. This leads to the loss of toponym semantic integrity and deviations in geographic entity recognition. This study proposes a set of Chinese toponym annotation specifications that integrate spatial semantics. By leveraging the XML markup language, it deeply combines the spatial location characteristics of toponyms with linguistic features, and designs fine-grained annotation rules to address the limitations of traditional methods in semantic integrity and geographic entity recognition. On this basis, by integrating multi-source corpora from the Encyclopedia of China: Chinese Geography and People’s Daily, a large-scale Chinese toponym annotation corpus (CHTopo) covering five major categories of toponyms has been constructed. The performance of this annotated corpus was evaluated through toponym recognition, exploring the construction methods of a large-scale, diversified, and high-coverage Chinese toponym annotated corpus from the perspectives of applicability and practicality. CHTopo is conducive to providing foundational support for geographic information extraction, spatial knowledge graphs, and geoparsing research, bridging linguistic and geospatial intelligence. Full article

Background: Awake craniotomy (AC) surgeries are less common in the pediatric population in comparison to their adult counterparts. Nonetheless, they can be considered for selected cases whereby speech preservation is paramount during maximal safe resection of intracranial lesions. We describe a case of AC for the excision of a brain arteriovenous malformation (bAVM) with language mapping in a pediatric patient. Methods: A previously well 16-year-old male presented with a spontaneous left frontal intracranial hemorrhage. Neuroimaging confirmed the cause to be a left antero-temporal bAVM centered in the insula. A decision was made for AC bAVM excision with language mapping for speech preservation. Results: As part of the pre-operative preparation, the patient and his caregivers were reviewed by a multidisciplinary team. For the conduct of the AC, the asleep–awake–asleep technique was used with processed EEG to guide anesthesia management. Additional modifications to make the patient comfortable included the avoidance of rigid cranial skull pins, urinary catheterization and central line insertion at the start of the surgery. Conclusions: Our experience concurs with the evidence that AC in children is a feasible option for select individuals. To our knowledge, this is the first detailed case description of a pediatric patient undergoing AC with language mapping for a bAVM. Emphases include a strong rapport between the patient and the managing multidisciplinary team, flexibility to adjust conventional workflows and limitations of neuroimaging adjuncts. Full article

Studies underscore the significance of coir fibers as a sustainable building material. Based on these insights, this research aims to evaluate coir fiber composite panels of various thicknesses as eco-friendly sound absorbing alternatives to synthetic construction materials like rockwool and fiberglass, aligning its use with the United Nations Sustainable Development Goals. Acoustic absorption was quantified with an impedance tube, and subsequent simulations compared the performance of coir composite panels with that of conventional materials, which constitutes an underexplored evaluation. Using 10 receiver points, the simulations reproduced the acoustic conditions of a multipurpose auditorium before and after the coir covering of parts of the rear and posterior walls. The results indicate that when coir coverings account for approximately 10% of the auditorium surface, reverberation times at 250, 500, 2000, and 4000 Hz are reduced by roughly 1 s. Furthermore, the outcomes reveal that early reflections occur more rapidly in the coir-enhanced model, while the values of the early decay time parameter decrease across all receiver points. Although the original configuration had poor speech clarity, the modified model achieved optimal values at all the measurement locations. These findings underscore the potential of coir fiber panels in enhancing acoustic performance while fostering sustainable construction practices. Full article

Pseudowords are essential in (psycho)linguistic research, offering a way to study language without meaning interference. Various methods for creating pseudowords exist, but each has its limitations. Traditional approaches modify existing words, risking unintended recognition. Modern algorithmic methods use high-frequency n-grams or syllable deconstruction but often require specialized expertise. Currently, no automatic process for pseudoword generation is designed explicitly for Greek, which is our primary focus. Therefore, we developed SyBig-r-Morph, a novel application that constructs pseudowords using syllables as the main building block, replicating Greek phonotactic patterns. SyBig-r-Morph draws input from word lists and databases that include syllabification, word length, part of speech, and frequency information. It categorizes syllables by position to ensure phonotactic consistency with user-selected morphosyntactic categories and can optionally assign stress to generated words. Additionally, the tool uses multiple lexicons to eliminate phonologically invalid combinations. Its modular architecture allows easy adaptation to other languages. To further evaluate its output, we conducted a manual assessment using a tool that verifies phonotactic well-formedness based on phonological parameters derived from a corpus. Most SyBig-r-Morph words passed the stricter phonotactic criteria, confirming the tool’s sound design and linguistic adequacy. Full article

This article reads Alcibiades’ speech in Plato’s Symposium in terms of the later Foucault’s examination of ‘parrhēsia’, or ‘frank spokenness’. It contends that, in part, Alcibiades’ stress on the sheer particularity and individuality of erotic attraction—in his case, attraction to Socrates himself—acts as a kind of rejoinder to the ‘impersonal’ aspect of erôs highlighted in the famous speech of Socrates/Diotima. Full article

Extracting causal relations from natural language texts is crucial for uncovering causality, and most existing causal relation extraction models are single-task learning-based models, which can not comprehensively address attributes such as part-of-speech tagging and chunk analysis. However, the characteristics of words with multi-domains are more relevant for causal relation extraction, due to words such as adjectives, linking verbs, etc., bringing more noise data limiting the effectiveness of the single-task-based learning methods. Furthermore, causalities from diverse domains also raise a challenge, as existing models tend to falter in multiple domains compared to a single one. In light of this, we propose a multi-task sequence tagging model, MPC−CE, which utilizes more information about causality and relevant tasks to improve causal relation extraction in noised data. By modeling auxiliary tasks, MPC−CE promotes a hierarchical understanding of linguistic structure and semantic roles, filtering noise and isolating salient entities. Furthermore, the sparse sharing paradigm extracts only the most broadly beneficial parameters by pruning redundant ones during training, enhancing model generalization. The empirical results on two datasets show 2.19% and 3.12% F1 improvement, respectively, compared to baselines, demonstrating that our proposed model can effectively enhance causal relation extraction with semantic features across multiple syntactic tasks, offering the representational power to overcome pervasive noise and cross-domain issues. Full article

Spoken Language Identification (SLID)-based applications have become increasingly important in everyday life, driven by advancements in artificial intelligence and machine learning. Multilingual countries utilize the SLID method to facilitate speech detection. This is accomplished by determining the language of the spoken parts using language recognizers. On the other hand, when working with multilingual datasets, the presence of multiple languages that have a shared origin presents a significant challenge for accurately classifying languages using automatic techniques. Further, one more challenge is the significant variance in speech signals caused by factors such as different speakers, content, acoustic settings, language differences, changes in voice modulation based on age and gender, and variations in speech patterns. In this study, we introduce the DBODL-MSLIS approach, which integrates biomimetic optimization techniques inspired by natural intelligence to enhance language classification. The proposed method employs Dung Beetle Optimization (DBO) with Deep Learning, simulating the beetle’s foraging behavior to optimize feature selection and classification performance. The proposed technique integrates speech preprocessing, which encompasses pre-emphasis, windowing, and frame blocking, followed by feature extraction utilizing pitch, energy, Discrete Wavelet Transform (DWT), and Zero crossing rate (ZCR). Further, the selection of features is performed by DBO algorithm, which removes redundant features and helps to improve efficiency and accuracy. Spoken languages are classified using Bayesian optimization (BO) in conjunction with a long short-term memory (LSTM) network. The DBODL-MSLIS technique has been experimentally validated using the IIIT Spoken Language dataset. The results indicate an average accuracy of 95.54% and an F-score of 84.31%. This technique surpasses various other state-of-the-art models, such as SVM, MLP, LDA, DLA-ASLISS, HMHFS-IISLFAS, GA base fusion, and VGG-16. We have evaluated the accuracy of our proposed technique against state-of-the-art biomimetic computing models such as GA, PSO, GWO, DE, and ACO. While ACO achieved up to 89.45% accuracy, our Bayesian Optimization with LSTM outperformed all others, reaching a peak accuracy of 95.55%, demonstrating its effectiveness in enhancing spoken language identification. The suggested technique demonstrates promising potential for practical applications in the field of multi-lingual voice processing. Full article

Good nutrition promotes a healthy population and mitigates the risk of disease. Integrating nutrition care in the primary healthcare system is considered an essential plan of action to manage poor nutritional status in the population. The role of primary healthcare professionals (HCPs) in the delivery of nutrition care is especially crucial due to a current lack of dietitians and dietary support in the primary care setting in Ireland. This qualitative research explored the current practice, barriers, facilitators, and feasible solutions proposed to optimize the provision of nutrition care by primary HCPs. Twenty semi-structured interviews (pharmacists (n = 14), dietitians (n = 3), a physiotherapist (n = 1), a speech and language therapist (n = 1), and a healthcare assistant (n = 1) were conducted. Six themes were derived from the data: current practice of nutrition care in primary care, perceived role, barriers and facilitators, the importance of a multidisciplinary patient-centred approach, training needs and preferences, and addressing barriers. Participants acknowledged the importance of nutrition care in clinical practice, the principal role of the dietitian as part of the multidisciplinary team, and the essential clinical competencies and nutrition training models that may facilitate the provision of nutrition care in primary practice. A paradigm shift to a multidisciplinary care model that prioritises the integration of nutrition care into primary care practice to ensure optimal dietary counselling is afforded to patients is essential. Full article

Speech lie detection is a technique that analyzes speech signals in detail to determine whether a speaker is lying. It has significant application value and has attracted attention from various fields. However, existing speech lie detection algorithms still have certain limitations. These algorithms fail to fully explore manually extracted features based on prior knowledge and also neglect the dynamic characteristics of speech as well as the impact of temporal context, resulting in reduced detection accuracy and generalization. To address these issues, this paper proposes a multi-feature speech lie detection algorithm based on the dual-stream deep architecture (DDA-MSLD).This algorithm employs a dual-stream structure to learn different types of features simultaneously. Firstly, it combines a gated recurrent unit (GRU) network with the attention mechanism. This combination enables the network to more comprehensively capture the context of speech signals and focus on the parts that are more critical for lie detection. It can perform in-depth sequence pattern analysis on manually extracted static prosodic features and nonlinear dynamic features, obtaining high-order dynamic features related to lies. Secondly, the encoder part of the transformer is used to simultaneously capture the macroscopic structure and microscopic details of speech signals, specifically for high-precision feature extraction of Mel spectrogram features of speech signals, obtaining deep features related to lies. This dual-stream structure processes various features of speech simultaneously, describing the subjective state of speech signals from different perspectives and thereby improving detection accuracy and generalization. Experiments were conducted on the multi-person scenario lie detection dataset CSC, and the results show that this algorithm outperformed existing state-of-the-art algorithms in detection performance. Considering the significant differences in lie speech in different lying scenarios, and to further evaluate the algorithm’s generalization performance, a single-person scenario Chinese lie speech dataset Local was constructed, and experiments were conducted on it. The results indicate that the algorithm has a strong generalization ability in different scenarios. Full article

In recent years, text classification has found wide application in diverse real-world scenarios. In Chinese news classification tasks, limitations such as sparse contextual information and semantic ambiguity exist in the title text. To improve the performance of short text classification, this paper proposes a Word2Vec-based enhanced word embedding method and exhibits the design of a dual-channel hybrid neural network architecture to effectively extract semantic features. Specifically, we introduce a novel weighting scheme, Term Frequency-Document Frequency Category-Distribution Weight (TF-IDF-CDW), where Category Distribution Weight (CDW) reflects the distribution pattern of words across different categories. By weighting the pretrained Word2Vec vectors with TF-IDF-CDW and concatenating them with part-of-speech (POS) feature vectors, semantically enriched and more discriminative word embedding vectors are generated. Furthermore, we propose a dual-channel hybrid model based on a Gated Convolutional Neural Network (GCNN) and Bidirectional Long Short-Term Memory (BiLSTM), which jointly captures local features and long-range global dependencies. To evaluate the overall performance of the model, experiments were conducted on the Chinese short text datasets THUCNews and TNews. The proposed model achieved classification accuracies of 91.85% and 87.70%, respectively, outperforming several comparative models and demonstrating the effectiveness of the proposed method. Full article

The use of hearing technology is one of the most effective strategies for providing access to spoken language and the auditory environment for students who are deaf or hard of hearing. In recent years, rapid advancements in hearing technologies have significantly improved access to spoken languages for learners of all ages. As part of the Special Issue “Educating Deaf Students in the 21st Century: A Changed and Changing Context”, this article describes how changes in hearing technology are related to changes in where and how students who are deaf or hard of hearing are educated. This article is designed to provide a foundation of knowledge about today’s hearing technologies for families, educators, and professionals such as speech–language pathologists or early childhood educators who support families and students. It provides an overview of hearing technology options, how they are prescribed and fit, and how benefits for language and literacy development can be evaluated. Barriers to effective use and future directions for hearing technologies are also described. The section “Highlights for Educators and Families” in the article discusses the practical application of this information to the work of those supporting students who are deaf or hard of hearing at home, at school, and in the community. Full article

The turn away from phenomenology in 20th century French philosophy was in large part due to an increased emphasis on Ferdinand de Saussure’s notion of “linguistic structure”, i.e., that language is the internal system of differences between signs. Thinkers such as Paul Ricoeur and Jean-François Lyotard famously offered a “semiological challenge” to phenomenology. The idea was that phenomenology, especially Merleau-Ponty’s phenomenology, reduces to the sensible world and cannot think linguistic structure. Thus, the argument goes that phenomenology leaves out a basic element of human life: not only can it not think linguistic structure, but it also cannot think about elements, e.g., writing and text, which are its result. This paper takes up this challenge, especially in reference to Merleau-Ponty’s terminology in Phenomenology of Perception of “speaking speech” (parole parlante) and “spoken speech” (parole parlée). I point out that, in retrospect of his later work, Merleau-Ponty very clearly did want to take linguistic structure seriously. This, however, means that we need to reconsider some of the basic themes in his work. Taking inspiration from the recently published “problem of speech” lectures, I reconstruct Merleau-Ponty’s idea that speech is a concrete limit situation from which we get both the idea of a language structure in which there are differences and of an ontological difference between being and beings. This is an internal criticism of both linguistic structure and formal ontology. I begin the paper by noting that, in Merleau-Ponty’s descriptions of the tacit and spoken cogito, also in Phenomenology of Perception, Merleau-Ponty criticizes the notion of a subject to which language refers and highlights the notion of a subject that defies representational and denotational structure. I do not, however, go along with Merleau-Ponty’s own criticism of the tacit ego, which he ultimately declared too subjectivistic. Ultimately, I hope to stress the importance of linguistic structure and writing in Merleau-Ponty’s ontology. This is an ontology of that is fragile and requires symbolization. This paper emphasizes under-developed themes in Merleau-Ponty’s work such as bodily event, difference, symbolization, and the writing of philosophy. Full article

Background: Early Hearing Detection and Intervention (EHDI) is essential for minimising the negative impact of childhood hearing loss on speech, language, and cognitive development. However, in low- and middle-income countries such as South Africa, various challenges hinder the implementation of EHDI services, leading to delayed diagnosis and intervention. Aim: This study explores caregivers’ experiences with EHDI services, identifying key challenges and facilitators affecting access and timely intervention. Methods: A narrative inquiry approach was used as part of a broader research initiative on family-centred EHDI. Nine caregivers of children who are deaf or hard of hearing (DHH) were purposively sampled, and data were collected through semi-structured interviews. Results: Thematic analysis revealed systemic and structural challenges, logistical and financial constraints, and caregiver-related factors that hindered access to EHDI services. Key facilitators included caregiver knowledge and advocacy, family support services such as counselling and South African Sign Language training, and high-quality audiological and educational services. Conclusions: Findings emphasise the need for policy-driven reforms, including expanding newborn hearing screening programmes, improving financial assistance mechanisms, and increasing public awareness. Addressing these challenges and leveraging facilitators can help South Africa align with global EHDI benchmarks and improve outcomes for DHH children. Full article