Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition

• URL: http://arxiv.org/abs/2507.10827v2
• Date: Sun, 20 Jul 2025 14:35:26 GMT
• Title: Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition
• Authors: Mengzhe Geng, Patrick Littell, Aidan Pine, PENÁĆ, Marc Tessier, Roland Kuhn,
• Abstract summary: The SENCOTEN language, spoken on the Saanich peninsula of southern Vancouver Island, is in the midst of vigorous language revitalization efforts.<n>We propose an ASR-driven documentation pipeline that leverages augmented speech data from a text-to-speech system.<n>An n-gram language model is also incorporated via shallow fusion or n-best restoring to maximize the use of available data.
• Score: 4.702636570667311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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