Dysarthria is a motor speech disorder often characterized by reduced speech
intelligibility through slow, uncoordinated control of speech production
muscles. Automatic Speech recognition (ASR) systems may help dysarthric talkers
communicate more effectively. To have robust dysarthria-specific ASR,
sufficient training speech is required, which is not readily available. Recent
advances in Text-To-Speech (TTS) synthesis multi-speaker end-to-end TTS systems
suggest the possibility of using synthesis for data augmentation. In this
paper, we aim to improve multi-speaker end-to-end TTS systems to synthesize
dysarthric speech for improved training of a dysarthria-specific DNN-HMM ASR.
In the synthesized speech, we add dysarthria severity level and pause insertion
mechanisms to other control parameters such as pitch, energy, and duration.
Results show that a DNN-HMM model trained on additional synthetic dysarthric
speech achieves WER improvement of 12.2% compared to the baseline, the addition
of the severity level and pause insertion controls decrease WER by 6.5%,
showing the effectiveness of adding these parameters. Audio samples are
available at

本文旨在通过使用多说话人端到端 TTS 合成系统为 DNN-HMM 建立训练提供合成语音，其中添加语音障碍严重性等控制参数以继续改进多语音说话人端到端 TTS 系统，实验表明：使用合成的语音，对 DNN-HMM ASR 进行训练可以将识别速率提高 12.2％，且添加严重性级别和暂停插入控制参数可以将错误率降低 6.5％。