Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition
This work addresses the challenge of building robust ASR systems for individuals with dysarthria, a motor speech disorder, by generating synthetic training data, though it is incremental as it builds on existing multi-speaker TTS methods.
The paper tackled the problem of insufficient training data for dysarthric speech recognition by synthesizing dysarthric speech using a multi-talker TTS system with added severity level and pause insertion controls, resulting in a 12.2% WER improvement for a DNN-HMM ASR model.
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