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Rapport (Rapport De Recherche) Année : 2021

Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks

Résumé

Automatic Speech Recognition (ASR) has made significant progress thanks to the advent of deep neural networks (DNNs). In the context of under-resourced languages, for which few resources are available, spectacular achievements has been reported. ASR systems are a step forward for language documentation, as the annotation cost is considerably reduced for field linguists (manually annotated an audio file can take a tremendous amount of time), and the language is preserved and perpetuated through documentation. Previous `standard' deep neural networks reached very good performances for phonemic transcription (such as with Kaldi and ESPnet approaches).However, these methods only rely on the phoneme-level. In this thesis, we explore recently published ASR approaches which have shown to be effective on low-resource languages to produce word-level audio-aligned transcriptions. The first approach, based on self-supervised learning, is a speech model that uses a Connectionist Temporal Classification (CTC). The second, entitled wav2vec-U, proposes a framework intended to build an ASR system in a fully unsupervised fashion. With few resources at our disposal, we try to assess the usability that can be made from dictionaries. We conducted experiments on two low-resource corpora, the Yongning Na and the Japhug from the Pangloss Collection. The experimental results from the first approach demonstrate powerful word-level transcriptions with competitive error rates. Preliminary results are reported on the second approach. By a coverage measure of dictionaries on the available transcriptions, we show that these resources are not yet usable in the conducted approaches.
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Dates et versions

hal-03429051 , version 1 (15-11-2021)

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Paternité - Partage selon les Conditions Initiales

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  • HAL Id : hal-03429051 , version 1

Citer

Cécile Macaire. Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks. [Research Report] LACITO (UMR 7107). 2021. ⟨hal-03429051⟩
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