Enhancing intelligibility of dysarthric speech using gated convolutional-based voice conversion system

Chen Yu Chen, Wei Zhong Zheng, Syu Siang Wang, Yu Tsao, Pei Chun Li, Ying Hui Lai

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

The voice conversion (VC) system is a well-known approach to improve the communication efficiency of patients with dysarthria. In this study, we used a gated convolutional neural network (Gated CNN) with the phonetic posteriorgrams (PPGs) features to perform VC for patients with dysarthria, with WaveRNN vocoder used to synthesis converted speech. In addition, two well-known deep learning-based models, convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) were used to compare with the Gated CNN in the proposed VC system. The results from the evaluation of speech intelligibility metric of Google ASR and listening test showed that the proposed system performed better than the original dysarthric speech. Meanwhile, the Gated CNN model performs better than the other models and requires fewer parameters compared to BLSTM. The results suggested that Gated CNN can be used as a communication assistive system to overcome the degradation of speech intelligibility caused by dysarthria.

Original languageEnglish
Pages (from-to)4686-4690
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2020-October
DOIs
StatePublished - 2020
Event21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
Duration: 25 Oct 202029 Oct 2020

Keywords

  • Deep learning
  • Dysarthric speech
  • Patients with dysarthria
  • Speech intelligibility
  • Voice conversion

Fingerprint

Dive into the research topics of 'Enhancing intelligibility of dysarthric speech using gated convolutional-based voice conversion system'. Together they form a unique fingerprint.

Cite this