Anti-stammering algorithm with adapted multi-layer perceptron
Stuttering (or stammering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. In this study, we suggested an efficient algorithm to perform stammering corrections (anti-stammering). This algorithm includes an effective feature extraction approach and a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25732/ http://journalarticle.ukm.my/25732/1/12.pdf |
| Summary: | Stuttering (or stammering) is a common speech disorder that may continue until adulthood, if not treated in its early stages. In this study, we suggested an efficient algorithm to perform stammering corrections (anti-stammering). This algorithm includes an effective feature extraction approach and an adapted classifier. We introduced Enhanced 1D Local Binary Patterns (EOLBP) for the extraction of features and adapted a classifier of Multi-Layer Perceptron (MLP) neural network for regression. This paper uses a database that involves speech signals with stammering, it can be called the Fluency Bank (FB). The result reveals that the proposed anti-stammering algorithm obtains promising achievement, where a high accuracy of 97.22% is attained. |
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