Next Article in Journal
Four-Level Quasi-Nested Inverter Topology for Single-Phase Applications
Next Article in Special Issue
The Impact of State-of-the-Art Techniques for Lossless Still Image Compression
Previous Article in Journal
A 560 GHz Sub-Harmonic Mixer Using Half-Global Design Method
Previous Article in Special Issue
A GAN-Based Video Intra Coding
Article

Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching

1
Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, 195029 St. Petersburg, Russia
2
Division of Information Systems, School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
3
Center for Language Research, School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu 965-8580, Japan
*
Authors to whom correspondence should be addressed.
All authors contributed equally to this work.
Electronics 2021, 10(3), 235; https://doi.org/10.3390/electronics10030235
Received: 22 November 2020 / Revised: 22 December 2020 / Accepted: 28 December 2020 / Published: 20 January 2021
(This article belongs to the Special Issue Recent Advances in Multimedia Signal Processing and Communications)
This article contributes to the discourse on how contemporary computer and information technology may help in improving foreign language learning not only by supporting better and more flexible workflow and digitizing study materials but also through creating completely new use cases made possible by technological improvements in signal processing algorithms. We discuss an approach and propose a holistic solution to teaching the phonological phenomena which are crucial for correct pronunciation, such as the phonemes; the energy and duration of syllables and pauses, which construct the phrasal rhythm; and the tone movement within an utterance, i.e., the phrasal intonation. The working prototype of StudyIntonation Computer-Assisted Pronunciation Training (CAPT) system is a tool for mobile devices, which offers a set of tasks based on a “listen and repeat” approach and gives the audio-visual feedback in real time. The present work summarizes the efforts taken to enrich the current version of this CAPT tool with two new functions: the phonetic transcription and rhythmic patterns of model and learner speech. Both are designed on a base of a third-party automatic speech recognition (ASR) library Kaldi, which was incorporated inside StudyIntonation signal processing software core. We also examine the scope of automatic speech recognition applicability within the CAPT system workflow and evaluate the Levenstein distance between the transcription made by human experts and that obtained automatically in our code. We developed an algorithm of rhythm reconstruction using acoustic and language ASR models. It is also shown that even having sufficiently correct production of phonemes, the learners do not produce a correct phrasal rhythm and intonation, and therefore, the joint training of sounds, rhythm and intonation within a single learning environment is beneficial. To mitigate the recording imperfections voice activity detection (VAD) is applied to all the speech records processed. The try-outs showed that StudyIntonation can create transcriptions and process rhythmic patterns, but some specific problems with connected speech transcription were detected. The learners feedback in the sense of pronunciation assessment was also updated and a conventional mechanism based on dynamic time warping (DTW) was combined with cross-recurrence quantification analysis (CRQA) approach, which resulted in a better discriminating ability. The CRQA metrics combined with those of DTW were shown to add to the accuracy of learner performance estimation. The major implications for computer-assisted English pronunciation teaching are discussed. View Full-Text
Keywords: speech processing; computer-assisted pronunciation training (CAPT); voice activity detection (VAD); audio-visual feedback; time warping (DTW); cross-recurrence quantification analysis (CRQA) speech processing; computer-assisted pronunciation training (CAPT); voice activity detection (VAD); audio-visual feedback; time warping (DTW); cross-recurrence quantification analysis (CRQA)
Show Figures

Figure 1

MDPI and ACS Style

Bogach, N.; Boitsova, E.; Chernonog, S.; Lamtev, A.; Lesnichaya, M.; Lezhenin, I.; Novopashenny, A.; Svechnikov, R.; Tsikach, D.; Vasiliev, K.; Pyshkin, E.; Blake, J. Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching. Electronics 2021, 10, 235. https://doi.org/10.3390/electronics10030235

AMA Style

Bogach N, Boitsova E, Chernonog S, Lamtev A, Lesnichaya M, Lezhenin I, Novopashenny A, Svechnikov R, Tsikach D, Vasiliev K, Pyshkin E, Blake J. Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching. Electronics. 2021; 10(3):235. https://doi.org/10.3390/electronics10030235

Chicago/Turabian Style

Bogach, Natalia, Elena Boitsova, Sergey Chernonog, Anton Lamtev, Maria Lesnichaya, Iurii Lezhenin, Andrey Novopashenny, Roman Svechnikov, Daria Tsikach, Konstantin Vasiliev, Evgeny Pyshkin, and John Blake. 2021. "Speech Processing for Language Learning: A Practical Approach to Computer-Assisted Pronunciation Teaching" Electronics 10, no. 3: 235. https://doi.org/10.3390/electronics10030235

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop