An Investigation to Identify Optimal Setup for Automated Assessment of Dysarthric Intelligibility using Deep Learning Technologies

Abstract

Recent advances in deep learning have provided an opportunity to improve and automate dysarthria intelligibility assessment, offering a cost-effective, accessible, and less subjective way to assess dysarthric speakers. However, reviewing previous literature in the area determines that the generalization of results on new dysarthric patients was not measured properly or incomplete among the previous studies that yielded very high accuracies due to the gaps in the adopted evaluation methodologies. This is of particular importance as any practical and clinical application of intelligibility assessment approaches must reliably generalize on new patients; otherwise, the clinicians cannot accept the assessment results provided by the system deploying the approach. In this paper, after these gaps are explained, we report on our extensive investigation to propose a deep learning–based dysarthric intelligibility assessment optimal setup. Then, we explain different evaluation strategies that were applied to thoroughly verify how the optimal setup performs with new speakers and across different classes of speech intelligibility. Finally, a comparative study was conducted, benchmarking the performance of our proposed optimal setup against the state of the art by adopting similar strategies previous studies employed. Results indicate an average of 78.2% classification accuracy for unforeseen low intelligibility speakers, 40.6% for moderate intelligibility speakers, and 40.4% for high intelligibility speakers. Furthermore, we noticed a high variance of classification accuracies among individual speakers. Finally, our proposed optimal setup delivered an average of 97.19% classification accuracy when adopting a similar evaluation strategy used by the previous studies.

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

“…For alleviating the above-mentioned limitations, a number of supportive systems to assess dysarthria via patient's speech or vocalperformance analysis have been proposed in literature. Examples include [12,25,26] which investigate the feasibility of machine learning (ML) methods for the analysis of audio-data collected in hospital and home scenarios. In [27,28], a telemonitoring-based application is introduced to automatically assess the evolution in the intelligibility of the speech of dysarthric patients.…”

Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, And Intelligibility Assessment

“…In the best situation, the proposed system gave an accuracy of 93.97% under the speaker-dependent scenario and 49.22% under the speaker independent scenario for the UA-Speech database. Hall et al in [22] reported the optimal setup of deep learning-based dysarthric intelligibility assessment and explained different evaluation strategies.…”

Profiles of Dysarthria: Clinical Assessment and Treatment

“…• As a recent technical development, the application of machine learning technology in dysarthria research has increased enormously, with a growth of about 900% since the beginning of this century. However, despite the great promise of this powerful technology, reports on standard applications in clinical care are still lacking and approaches based on methodologically rigorous models still yield disappointing results [14,15]. Among the major obstacles that need to be overcome is the problem of how we can collect training datasets that guarantee accurate and unbiased predictions, particularly considering the large variability that exists across dysarthria types, disease stages, degrees of severity, and individual idiosyncrasies; not to mention the variability as a function of age and gender, dialectal variants, ethnicities, mood, motivation, or factors related to the quality of acoustic data.…”