Phase-Based Feature Representations for Improving Recognition of Dysarthric Speech

Abstract

Dysarthria is a neurological speech impairment, which usually results in the loss of motor speech control due to muscular atrophy and incoordination of the articulators. As a result the speech becomes less intelligible and difficult to model by machine learning algorithms due to inconsistencies in the acoustic signal and data sparseness. This paper presents phase-based feature representations for dysarthric speech that are exploited in the group delay spectrum. Such representations are found to be better suited to characterising the resonances of the vocal tract, exhibit better phone discrimination capabilities in dysarthric signals and consequently improve ASR performance. All the experiments were conducted using the UASPEECH corpus and significant ASR gains are reported using phase-based cepstral features in comparison to the standard MFCCs irrespective of the severity of the condition.