Accounting for lexical tones when modeling phonological distance

Do, Youngah; Lai, KY

doi:10.1353/lan.2021.0008

Cited by 6 publications

(5 citation statements)

References 47 publications

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“…To assess the recognition quality, the distance metric was calculated as Levenstein distance L, which shows how much two phoneme sequences differ [60]. Metric for sequences of equal length (e.g., Hamming distance) cannot be used in this task, because two sequences can differ in their length due to possible substitutions, deletions, or insertions of phonemes.…”

Section: Dspcore Assessment With Reference Corporamentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Dspcore Assessment With Reference Corporamentioning

confidence: 99%

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

et al. 2021

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“…This work did not explore representations based on phonological features, given that their incorporation has failed to provide evaluative improvements in other studies of computational phonotactics (Mayer and Nelson, 2020;Mirea and Bicknell, 2019;Pimentel et al, 2020). However, feature-based approaches can be both theoretically insightful and may even prove necessary for other quantifications, such as the measure of phonological distance where tone is involved (Do and Lai, 2021a).…”

Section: Shortcomings and Extensionsmentioning

confidence: 99%

“…Choice of ordering is sometimes justified based on segment-tone co-occurrence restrictions in the language under study (Myers and Tsay, 2005), but is often presented without justification (Kirby and Yu, 2007;, and in some cases tone is simply ignored (Gong, 2017). When the space of possibilities is considered, researchers generally select the permutation which maximizes model fit to some external data, such as participant judgments of phonological distance (Do and Lai, 2021a) or wordlikeness (Do and Lai, 2021b).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

2021

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Reducing communication breakdown is critical to success in interactive NLP applications, such as dialogue systems. To this end, we propose a confusion-mitigation framework for the detection and remediation of communication breakdown. In this work, as a first step towards implementing this framework, we focus on detecting phonemic sources of confusion. As a proof-of-concept, we evaluate two neural architectures in predicting the probability that a listener will misunderstand phonemes in an utterance. We show that both neural models outperform a weighted n-gram baseline, showing early promise for the broader framework.

show abstract

Section: Shortcomings and Extensionsmentioning

confidence: 99%

“…Choice of ordering is sometimes justified based on segment-tone co-occurrence restrictions in the language under study (Myers and Tsay, 2005), but is often presented without justification (Kirby and Yu, 2007;Yang et al, 2018), and in some cases tone is simply ignored (Gong, 2017). When the space of possibilities is considered, researchers generally select the permutation which maximizes model fit to some external data, such as participant judgments of phonological distance (Do and Lai, 2021a) or wordlikeness (Do and Lai, 2021b).…”

Section: Introductionmentioning

confidence: 99%

Incorporating tone in the calculation of phonotactic probability

Kirby¹

2021

Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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This paper investigates how the ordering of tone relative to the segmental string influences the calculation of phonotactic probability. Trigram and recurrent neural network models were trained on syllable lexicons of four Asian syllable-tone languages (Mandarin, Thai, Vietnamese, and Cantonese) in which tone was treated as a segment occurring in different positions in the string. For trigram models, the optimal permutation interacted with language, while neural network models were relatively unaffected by tone position in all languages. In addition to providing a baseline for future evaluation, these results suggest that phonotactic probability is robust to choices of how tone is ordered with respect to other elements in the syllable.

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Accounting for lexical tones when modeling phonological distance

Cited by 6 publications

References 47 publications

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

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

Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

Incorporating tone in the calculation of phonotactic probability

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