Automation Detection Of Stuttering Using MFCC In Conjunction With CSO
DOI:
https://doi.org/10.29284/dxvb7r41Keywords:
Cuckoo Search Optimization (CSO), Mel-Frequency Cepstral Coefficients (MFCC), Stuttering Detection etc.Abstract
Over 79.5 million people throughout the world suffer with stuttering, which is typified by prolongations, quiet pauses, and repeating noises. It stems from a neurological mismatch between speech intention and production, with idiopathic cases being common. This paper discusses the connection between stuttering and mental health issues, particularly social isolation, which is worsened by anxiety but not directly caused by it. Diagnosis involves speech-language pathologists assessing disfluencies through specific tests. Treatment aims to minimize the impact of stuttering using techniques like stuttering modification and fluency shaping therapies. The paper also explores enhancing stuttering detection using Mel-Frequency Cepstral Coefficients (MFCC) coupled with Cuckoo Search Optimization (CSO), emphasizing the methodology to improve diagnosis accuracy. The paper is about research gaps that exist in effective detection, diverse datasets, and the investigation of multimodal aspects to enhance detection systems, even with high accuracy metrics.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
