用语音合成与跨语言迁移技术，提升濒危语种的自动语音识别准确率。
Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition
- 结合语音合成生成数据，利用跨语言模型迁移知识。
- 在有限语料下实现19.34%词错误率，过滤后降至14.32%。
- 适合语言复兴团队、数字人文研究者及低资源语音技术开发者。
SENCOTEN语言是加拿大温哥华岛萨尼奇半岛的原住民语言，正面临殖民政策导致的语言消亡危机。为支持社区的语言复兴工作，数字化技术成为关键。自动语音识别（ASR）有望加速语言记录与教育资源建设。然而，由于数据稀缺以及该语言复杂的多词素结构和重音驱动的音变现象，开发其ASR系统极具挑战。为此，我们提出一种基于ASR的文档化流程：利用文本转语音（TTS）系统增强语音数据，并通过语音基础模型（SFMs）进行跨语言迁移学习；同时引入n-gram语言模型，通过浅融合或n-best还原方式最大化现有数据利用。在SENCOTEN数据集上的实验表明，测试集词错误率（WER）达19.34%，字符错误率（CER）为5.09%，未登录词率高达57.02%。剔除少量字母ç相关错误后，WER降至14.32%（未见词上为26.48%），CER降至3.45%，验证了该流程对语言文档化的有效潜力。
原文摘要 · Abstract (English)
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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