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Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition

arXiv 2507.10827 ↗First posted 14 Jul 2025cs.SD cs.CL eess.AScited by 0

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Abstract (arXiv)

The SENCOTEN language, spoken on the Saanich peninsula of southern Vancouver Island, is in the midst of vigorous language revitalization efforts to turn the tide of language loss as a result of colonial language policies. To support these on-the-ground efforts, the community is turning to digital technology. Automatic Speech Recognition (ASR) technology holds great promise for accelerating language documentation and the creation of educational resources. However, developing ASR systems for SENCOTEN is challenging due to limited data and significant vocabulary variation from its polysynthetic structure and stress-driven metathesis. To address these challenges, we propose an ASR-driven documentation pipeline that leverages augmented speech data from a text-to-speech (TTS) system and cross-lingual transfer learning with Speech Foundation Models (SFMs). An n-gram language model is also incorporated via shallow fusion or n-best restoring to maximize the use of available data. Experiments on the SENCOTEN dataset show a word error rate (WER) of 19.34% and a character error rate (CER) of 5.09% on the test set with a 57.02% out-of-vocabulary (OOV) rate. After filtering minor cedilla-related errors, WER improves to 14.32% (26.48% on unseen words) and CER to 3.45%, demonstrating the potential of our ASR-driven pipeline to support SENCOTEN language documentation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 46 references that are in the map. First posted on arXiv 14 Jul 2025.

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15 of 46 references.

References · 15

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  4. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  5. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  6. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,154
  7. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
  8. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 717
  9. 2015Listen, Attend and Spellcited by 543
  10. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 178
  11. 2017An analysis of incorporating an external language model into a sequence-to-sequence modelcited by 111
  12. 2023TorchAudio-Squim: Reference-less Speech Quality and Intelligibility measures in TorchAudiocited by 59
3 more
  1. 2024Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognitioncited by 9
  2. 2020User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpiscited by 5
  3. 2023Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognitioncited by 4

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