Work

Stammering Algorithm with Adapted Multi-Layer Perceptron

Year: 2024

Type: article

Abstract: 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.

Source: Jurnal Kejuruteraan

DOI: 10.17576/jkukm-2024-36(5)-12

Authors: Ali Bashar Hussein, Raid Rafi Omar Al-Nima, Tingting Han

Institutions: University of Technology and Applied Sciences, Muscat, University of Technology and Applied Sciences

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4403092842

FWCI: 0.147

Cites:

Cited by: 1

Related to: 10