Abstract
The SENÄOTEN 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 SENÄOTEN 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 SENÄOTEN 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 SENÄOTEN language documentation.
Abstract (translated)
SENĆOŦEN语言，一种在温哥华岛南部的萨尼奇半岛上使用的语言，正面临着由于殖民时期的语言政策导致的语言流失问题。为了扭转这一趋势，该社区正在积极进行语言复兴工作，并且开始借助数字技术来支持这些努力。自动语音识别（ASR）技术在这方面具有很大的潜力，可以加速语言记录和教育资源的创建。 然而，为SENĆOŦEN开发ASR系统面临诸多挑战，包括数据有限以及由于其多合成结构和重音驱动的元音互换导致词汇变化显著的问题。为了应对这些挑战，我们提出了一个基于ASR的语言文档流水线方法，这种方法利用了来自文本到语音（TTS）系统的增强语音数据，并结合跨语言迁移学习与语音基础模型（SFM）。此外，通过浅层融合或n-best恢复技术，我们将n-gram语言模型融入其中以最大限度地利用现有数据。 在SENĆOŦEN的数据集上进行的实验显示，在测试集中，词错误率为19.34%，字符错误率为5.09%。当考虑到26.48%未登录词汇（OOV）的情况下，单词错误率降至14.32%，字符错误率降至3.45%，这表明了我们提出的基于ASR的流水线方法在支持SENĆOŦEN语言记录方面具有巨大的潜力。
URL
https://arxiv.org/abs/2507.10827
