构建低资源语音平行语料库，助力小语种语音技术发展
Connecting Voices: LoReSpeech as a Low-Resource Speech Parallel Corpus
- 基于协作平台与MFA工具，分步对齐短音频与长文本语音
- 产出包含多语言对齐的LoReSpeech语料库，支持语音识别与翻译
- 适合小语种研究、语音技术普惠及语言保护项目使用
对齐语音语料是自动语音识别（ASR）和语音翻译等自然语言处理技术的基础，但对未充分代表的语言而言仍极为稀缺，制约了其技术应用。本文提出一种构建低资源语音到语音翻译语料库LoReSpeech的方法。首先通过协作平台创建包含短音频及其转录的子语料库LoReASR；在此基础上，利用MFA等工具对圣经等长篇语音内容进行对齐。LoReSpeech提供跨语言与同语言的双重对齐，可推动多语言ASR系统、端到端语音翻译模型的发展，并助力语言保护与数字包容性建设。本工作属于Tutlayt AI项目（https://tutlayt.fr）。
原文摘要 · Abstract (English)
Aligned audio corpora are fundamental to NLP technologies such as ASR and speech translation, yet they remain scarce for underrepresented languages, hindering their technological integration. This paper introduces a methodology for constructing LoReSpeech, a low-resource speech-to-speech translation corpus. Our approach begins with LoReASR, a sub-corpus of short audios aligned with their transcriptions, created through a collaborative platform. Building on LoReASR, long-form audio recordings, such as biblical texts, are aligned using tools like the MFA. LoReSpeech delivers both intra- and inter-language alignments, enabling advancements in multilingual ASR systems, direct speech-to-speech translation models, and linguistic preservation efforts, while fostering digital inclusivity. This work is conducted within Tutlayt AI project (https://tutlayt.fr).
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