Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network

Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network

Abstract In this paper, we use empirical mode decomposition and Hurst-based mode selection (EMDH) along with deep learning architecture using a convolutional neural network (CNN) to improve the recognition of dysarthric speech. The EMDH speech enhancement technique is used as a preprocessing step to...

Full description

Bibliographic Details
Main Authors: Mohammed Sidi Yakoub, Sid-ahmed Selouani, Brahim-Fares Zaidi, Asma Bouchair
Format: Article
Language:English
Published: SpringerOpen 2020-01-01
Series:EURASIP Journal on Audio, Speech, and Music Processing
Subjects:
Dysarthria
Empirical mode decomposition
Hurst mode selection
Convolutional neural network
Online Access:https://doi.org/10.1186/s13636-019-0169-5

Internet

https://doi.org/10.1186/s13636-019-0169-5

Similar Items