Speech intelligibility assessment of dysarthria using Fisher vector encoding. (January 2023)

• Record Type: Journal Article
• Authors: H․M․, Chandrashekar; Karjigi, Veena; Sreedevi, N.
• Is Part Of: Computer speech & language. Volume 77(2023)
• Publication Date: 2023-01
• Subjects: Dysarthria -- Intelligibility assessment -- Spectral domain representation -- Cepstral domain representation -- Fisher vector encoding -- Probabilistic linear discriminant analysis; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2022.101411
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Intelligibility assessment of dysarthric speech using UA and TORGO databases. Different frame-level features are extracted in from temporal, spectral, and cepstral domains. Temporal encoding and Fisher vector encoding techniques are used to convert frame-level features to utterance level features. Probabilistic linear discriminant analysis (PLDA) and artificial neural network (ANN) classifiers are used for the intelligibility assessment of dysarthric speech. Fisher vector encoding performance is superior to temporal encoding and ANN performance is superior to PLDA. Abstract: Neuromuscular disorders can lead to dysarthria. Dysarthria is a speech disorder that mainly affects the human motor speech system. It often results in reduced speech intelligibility. Speech intelligibility is one of the parameters to assess the severity of dysarthria. The intelligibility of speech decreases as the severity increases. Automatic speech intelligibility assessment could be reliable and cost-effective compared to the conventional methods which need experienced speech pathologists. Automatic intelligibility assessment includes feature extraction and classification. Features extracted from spectral and cepstral domains are investigated for use in intelligibility assessment. Frame level features are extracted from different signal representations in spectral and cepstral domains. Fisher vector encoding and temporal encoding that uses descriptive statistics are applied to convert frame level features into utterance level. These features are fed to PLDA and ANN classifiers for intelligibility assessment. Overall, the performance of Fisher vector encoding is found to be superior compared to temporal encoding. In all features, the performance of ANN is better in assessing the intelligibility levels compared to PLDA as expected. The STFT, as well as harmonics in spectral domain and CQCC in the cepstral domain, performed well for intelligibility assessment of unseen data of TORGO database.