Utterance Verification-Based Dysarthric Speech Intelligibility Assessment Using Phonetic Posterior Features

Abstract

In the literature, the task of dysarthric speech intelligibility assessment has been approached through development of different low-level feature representations, subspace modeling, phone confidence estimation or measurement of automatic speech recognition system accuracy. This paper proposes a novel approach where the intelligibility is estimated as the percentage of correct words uttered by a speaker with dysarthria by matching and verifying utterances of the speaker with dysarthria against control speakers' utterances in phone posterior feature space and broad phonetic posterior feature space. Experimental validation of the proposed approach on the UA-Speech database, with posterior feature estimators trained on the data from auxiliary domain and language, obtained a best Pearson's correlation coefficient (r) of 0.950 and Spearman's correlation coefficient (ρ) of 0.957. Furthermore, replacing control speakers' speech with speech synthesized by a neural text-to-speech system obtained a best r of 0.937 and ρ of 0.961.

Dynamic spectral characteristics of defective sounds pronunciation in students with minimal dysarthric disorders

“…In this regard, it can be claimed that specific defects in articulation at minimal dysarthric disorders lead to the appearance of qualitatively different changes in the spectral characteristics of sounds [14][15][16]. Currently, in Russian speech therapy, the linguistic aspect of sound-pronunciation disorders in children with this type of speech dysontogenesis is practically not developed.…”

Investigation of DNN-HMM and Lattice Free Maximum Mutual Information Approaches for Impaired Speech Recognition

“…A bidirectional Deep Recurrent Neural Network (biRNN) based DNN-HMM is used for phoneme recognition [15]. In a recent work [19], the authors used a phonetic posterior feature space for matching and verifying the impaired speech with the control speakers data. Several parameters such as Linear Discriminant Analysis (LDA), context dependent states, Feature space Maximum Liklihood Linear Regression (FMLLR) are used with Teacher-Student network [20] to increase the accuracy.…”