Many consumer speech recognition systems are not tuned for people with speech
disabilities, resulting in poor recognition and user experience, especially for
severe speech differences. Recent studies have emphasized interest in
personalized speech models from people with atypical speech patterns. We
propose a query-by-example-based personalized phrase recognition system that is
trained using small amounts of speech, is language agnostic, does not assume a
traditional pronunciation lexicon, and generalizes well across speech
difference severities. On an internal dataset collected from 32 people with
dysarthria, this approach works regardless of severity and shows a 60%
improvement in recall relative to a commercial speech recognition system. On
the public EasyCall dataset of dysarthric speech, our approach improves
accuracy by 30.5%. Performance degrades as the number of phrases increases, but
consistently outperforms ASR systems when trained with 50 unique phrases.

设计了一种基于 qery-by-example 的个人化短语识别系统，针对特殊人群的语音模式进行培训，实验数据表明，该系统相对于商用语音识别系统，无论严重程度都取得了 60% 的识别改进，当训练 50 个独特短语时，一致优于 ASR 系统。