Optimal implementation setting for computerized visualization cues in assisting L2 intonation production

Liu, Yeu Ting; Tseng, Wen Ta

doi:10.1016/j.system.2019.102145

Cited by 6 publications

(2 citation statements)

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“…Particularly, an idea to make use of visual input to extend the audio perception process has become easier to implement using mobile personalized solutions harnessing the power of modern portable devices which have become much more than simply communication tools. It has led to language learning environments which widely incorporate speech technologies [5], visualization and personalizing based on mobile software and hardware designs [19,61], which include our own efforts [21,22,62].…”

Section: Discussionmentioning

confidence: 99%

“…This approach follows the noticing hypothesis [18], which states that noticing is a necessary precursor to learning. The effects of different attentional states and contexts with their pedagogical implications have been further examined in [19] where the relation between the attention state and the stimuli were shown to vary with context. The complexity of phonological problems combined with simultaneously teaching learners with different phonological problems exacerbates the difficulty for classroom teachers to provide personalised feedback, particularly for those who teach large classes [20].…”

Section: Introductionmentioning

confidence: 99%

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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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