Typical ASR systems segment the input audio into utterances using purely acoustic information, which may not resemble the sentence-like units that are expected by conventional machine translation (MT) systems for Spoken Language Translation. In this work, we propose a model for correcting the acoustic segmentation of ASR models for low-resource languages to improve performance on downstream tasks. We propose the use of subtitles as a proxy dataset for correcting ASR acoustic segmentation, creating synthetic acoustic utterances by modeling common error modes. We train a neural tagging model for correcting ASR acoustic segmentation and show that it improves downstream performance on MT and audio-document cross-language information retrieval (CLIR).
翻译:典型的ASR系统使用纯声学信息,将音频输入语音中,这也许与传统机器翻译系统预期的口语翻译的类似句号单元不同,在这项工作中,我们提出了一个模型,用于纠正低资源语言的ASR模式的声学分解,以提高下游任务的业绩。我们建议使用字幕作为代用数据集,用于纠正ASR声学分解,通过模拟常见错误模式创建合成声学话。我们为纠正ASR声学分解开发了一个神经标记模型,并表明该模型提高了MT和声文件跨语言信息检索的下游性能。