Accurate synthesis of Dysarthric Speech for ASR data augmentation
Mohammad Soleymanpour Michael T. Johnson Rahim Soleymanpour Jeffrey Berry
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 can help dysarthric talkers
communicate more effectively. However, robust dysarthria-specific ASR requires
a significant amount of training speech, which is not readily available for
dysarthric talkers. This paper presents a new dysarthric speech synthesis
method for the purpose of ASR training data augmentation. Differences in
prosodic and acoustic characteristics of dysarthric spontaneous speech at
varying severity levels are important components for dysarthric speech
modeling, synthesis, and augmentation. For dysarthric speech synthesis, a
modified neural multi-talker TTS is implemented by adding a dysarthria severity
level coefficient and a pause insertion model to synthesize dysarthric speech
for varying severity levels. To evaluate the effectiveness for synthesis of
training data for ASR, dysarthria-specific speech recognition was used. Results
show that a DNN-HMM model trained on additional synthetic dysarthric speech
achieves WER improvement of 12.2% compared to the baseline, and that the
addition of the severity level and pause insertion controls decrease WER by
6.5%, showing the effectiveness of adding these parameters. Overall results on
the TORGO database demonstrate that using dysarthric synthetic speech to
increase the amount of dysarthric-patterned speech for training has significant
impact on the dysarthric ASR systems. In addition, we have conducted a
subjective evaluation to evaluate the dysarthric-ness and similarity of
synthesized speech. Our subjective evaluation shows that the perceived
dysartrhic-ness of synthesized speech is similar to that of true dysarthric
speech, especially for higher levels of dysarthria