Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition

• URL: http://arxiv.org/abs/2201.11571v1
• Date: Thu, 27 Jan 2022 15:22:09 GMT
• Title: Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition
• Authors: Mohammad Soleymanpour, Michael T. Johnson, Rahim Soleymanpour, Jeffrey Berry
• Abstract summary: Dysarthria is a motor speech disorder often characterized by reduced speech intelligibility. To have robust dysarthria-specific ASR, sufficient training speech is required. Recent advances in Text-To-Speech synthesis suggest the possibility of using synthesis for data augmentation.
• Score: 4.637732011720613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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