Document Type
Article
Publication Date
3-1-2022
Publication Title
Applied Sciences (Switzerland)
Volume
12
Issue
5
Keywords
Bandpass, Classifier, Cochlear implants, Deep learning, Filterbank, Gammatone, Voice pathology
Abstract
This paper presents a pathological voice identification system employing signal processing techniques through cochlear implant models. The fundamentals of the biological process for speech perception are investigated to develop this technique. Two cochlear implant models are considered in this work: One uses a conventional bank of bandpass filters, and the other one uses a bank of optimized gammatone filters. The critical center frequencies of those filters are selected to mimic the human cochlear vibration patterns caused by audio signals. The proposed system processes the speech samples and applies a CNN for final pathological voice identification. The results show that the two proposed models adopting bandpass and gammatone filterbanks can discriminate the pathological voices from healthy ones, resulting in F1 scores of 77.6% and 78.7%, respectively, with speech samples. The obtained results of this work are also compared with those of other related published works.
DOI
10.3390/app12052398
E-ISSN
20763417
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Islam, Rumana; Abdel-Raheem, Esam; and Tarique, Mohammed. (2022). A Novel Pathological Voice Identification Technique through Simulated Cochlear Implant Processing Systems. Applied Sciences (Switzerland), 12 (5).
https://scholar.uwindsor.ca/electricalengpub/487