标题： 使用自动语音识别支持SENCOTEN语言文档工作 显示英文标题

标题： Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition

摘要： 塞恩科滕语是在加拿大温哥华岛南部的萨尼奇半岛上使用的语言，目前正面临着激烈的语言复兴努力，以扭转由于殖民语言政策导致的语言流失。为了支持这些实地的努力，社区正在转向数字技术。自动语音识别（ASR）技术在加速语言记录和教育资料的创建方面具有巨大潜力。然而，由于数据有限以及由于其多合成结构和重音驱动的元音变化而导致的显著词汇差异，为塞恩科滕语开发ASR系统具有挑战性。为了解决这些挑战，我们提出了一种基于ASR的文档流程，利用文本到语音（TTS）系统生成的增强语音数据，并结合语音基础模型（SFMs）进行跨语言迁移学习。通过浅层融合或n最佳恢复，还集成了一个n-gram语言模型，以最大限度地利用可用数据。在塞恩科滕数据集上的实验显示，在测试集上词错误率（WER）为19.34%，字符错误率（CER）为5.09%，其中未见词的比例为57.02%。在过滤掉少量软音符相关的错误后，WER提高到14.32%（未见词为26.48%），CER提高到3.45%，这表明我们的基于ASR的流程在支持塞恩科滕语文档方面具有潜力。 显示英文摘要

摘要： 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.