Dysarthria is a manisfestation of the disruption in the neuromuscular physiology resulting in uneven, slow, slurred, harsh or quiet speech. Dysarthric speech poses serious challenges to automatic speech recognition, considering this speech is difficult to decipher for both humans and machines. The objective of this work is to enhance dysarthric speech features to match that of healthy control speech. We use a Time-Delay Neural Network based Denoising Autoencoder (TDNN-DAE) to enhance the dysarthric speech features. The dysarthric speech thus enhanced is recognized using a DNN-HMM based Automatic Speech Recognition (ASR) engine. This methodology was evaluated for speaker-independent (SI) and speaker-adapted (SA) systems. Absolute improvements of 13% and 3% was observed in the ASR performance for SI and SA systems respectively as compared with unenhanced dysarthric speech recognition.
Bhat et al. REF proposed using a time-delay neural network-based denoising autoencoder to enhance dysarthric speech features before performing DNN-HMM-based recognition.
Dysarthric Speech Recognition Using Time-delay Neural Network Based Denoising Autoencoder
INTERSPEECH
Computer Science