| Full text | |
| Author(s): |
Gupta, Siddhant
[1]
;
Patil, Ankur T.
[1]
;
Purohit, Mirali
[1]
;
Parmar, Mihir
[2]
;
Patel, Maitreya
[1]
;
Patil, Hemant A.
[1]
;
Guido, Rodrigo Capobianco
[3]
Total Authors: 7
|
| Affiliation: | [1] Dhirubhai Ambani Inst Informat & Commun Technol I, Speech Res Lab, Gandhinagar 382007 - India
[2] Arizona State Univ, Tempe, AZ - USA
[3] Sao Paulo State Univ, Unesp Univ Estadual Paulista, Inst Biociencias Letras & Ciencias Exatas, Rua Cristovao Colombo 2265, BR-15054000 Sao Jose Do Rio Preto, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | NEURAL NETWORKS; v. 139, p. 105-117, JUL 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Recently, we have witnessed Deep Learning methodologies gaining significant attention for severitybased classification of dysarthric speech. Detecting dysarthria, quantifying its severity, are of paramount importance in various real-life applications, such as the assessment of patients' progression in treatments, which includes an adequate planning of their therapy and the improvement of speech-based interactive systems in order to handle pathologically-affected voices automatically. Notably, current speech-powered tools often deal with short-duration speech segments and, consequently, are less efficient in dealing with impaired speech, even by using Convolutional Neural Networks (CNNs). Thus, detecting dysarthria severity-level based on short speech segments might help in improving the performance and applicability of those systems. To achieve this goal, we propose a novel Residual Network (ResNet)-based technique which receives short-duration speech segments as input. Statistically meaningful objective analysis of our experiments, reported over standard Universal Access corpus, exhibits average values of 21.35% and 22.48% improvement, compared to the baseline CNN, in terms of classification accuracy and F1-score, respectively. For additional comparisons, tests with Gaussian Mixture Models and Light CNNs were also performed. Overall, the values of 98.90% and 98.00% for classification accuracy and F1-score, respectively, were obtained with the proposed ResNet approach, confirming its efficacy and reassuring its practical applicability. (C) 2021 Elsevier Ltd. All rights reserved. (AU) | |
| FAPESP's process: | 19/04475-0 - Paraconsistent Feature Analysis of Speech Signals: fighting the voice spoofing attacks |
| Grantee: | Rodrigo Capobianco Guido |
| Support Opportunities: | Regular Research Grants |