Automatic Stutter Speech Recognition and Classification Using Hyper-Heuristic Search Algorithm

Abstract

Introduction: The normal flow of speech is disrupted by the repetition or delay of sounds or phrases in stuttering or stammering, a speech impairment. Speech flow becomes difficult when someone stutters. Recent advances in deep learning have changed the speech domain, while stuttering recognition has received very little attention.

Problem: The detection of stuttering is a fascinating area of research that encompasses pathology, psychology, and signal processing, making it challenging to detect. Stress, delays in early development, and other anomalies are the causes of stuttering. This too seems to be complicated and perplexing.

Methods: To overcome these problems, a feature of automatic Stutter speech recognition and the Stutter Audio classification with Stutter audio by deep learning using Hyper-Heuristic Search Algorithm for Stutter Audio signal analysis is employed. The study performs the sound digitalization in which the Analog signals to a digital signal by using sampling and quantization, the Mel Spectrogram algorithm is employed in the Audio classification, the Automated driving to medical devices is done in the Audio deep learning, the Hyper-parameter tuning is performed in the Feature Optimization, the Automatic Speech Recognition is implemented, in which here we use the Weighted Finite-State Transducer framework, the Perceptual Linear Prediction(PLP) and Viterbi search, the Discrimination training by deep neural networks and we implement the Hyper-heuristic Search Algorithm.

Results: Using the suggested system flow, the results analyse Automatic Speech Recognition and reaction time with the Classification accuracies of Stutter Audio signal analysis.

In summary: The model shows a predetermined order of heuristics that enhances the process of fixing stuttering issues. This study evaluates Automatic Speech Recognition (ASR) and Response Time with a higher accuracy rate and more efficacy.