论文标题
基于Chukchi的低资源语言的自动语音识别
Automatic Speech Recognition of Low-Resource Languages Based on Chukchi
论文作者
论文摘要
以下论文介绍了一个侧重于基于Chukchi语言的新自动语音识别（ASR）的项目。 Chukchi语言没有一个完整的语料库，因此，大多数工作都包括从开源来收集chukchi语言的音频和文本并处理它们。我们设法收集了21:34:23小时的录音和112,719个句子（或2,068,273个单词）的文字。 XLSR模型对获得的数据进行了培训，即使有少量数据，该数据也显示出良好的结果。除了Chukchi语言是一种低资源语言之外，它也是多合成的，这使任何自动处理都显着复杂。因此，用于评估ASR的常规指标对多合成语言的指示较少。但是，CER指标显示出良好的结果。多合成语言的指标问题仍然开放。
The following paper presents a project focused on the research and creation of a new Automatic Speech Recognition (ASR) based in the Chukchi language. There is no one complete corpus of the Chukchi language, so most of the work consisted in collecting audio and texts in the Chukchi language from open sources and processing them. We managed to collect 21:34:23 hours of audio recordings and 112,719 sentences (or 2,068,273 words) of text in the Chukchi language. The XLSR model was trained on the obtained data, which showed good results even with a small amount of data. Besides the fact that the Chukchi language is a low-resource language, it is also polysynthetic, which significantly complicates any automatic processing. Thus, the usual WER metric for evaluating ASR becomes less indicative for a polysynthetic language. However, the CER metric showed good results. The question of metrics for polysynthetic languages remains open.
