Dysarthric Severity Categorization Based on Speech Intelligibility: A Hybrid Approach

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Vidya M., Ganesh Vaidyanathan S.
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引用次数: 0

Abstract

The intelligibility of speech is a primary component to assess the severity level of Dysarthria, a speech disorder, which is caused not only due to weakness in vocal motor muscles but also difficulty in controlling its movements. Prior information about the severity of Dysarthria, aids the therapist during the rehabilitation process. This paper introduces a novel hybrid architecture comprising Gaussian Mixture Model and Neural Network (GMM-NN) for categorizing Dysarthric severity into four classes based on speech intelligibility. Mel Frequency Cepstral Coefficients (MFCC) extracted from the segmented speech signals are used to train the classifier. The proposed model produced a 1.9% improvement in accuracy when compared to the baseline Gaussian Mixture Model (GMM). The Gaussian Mixture Model Deep Neural Network (GMM-DNN) and Gaussian Mixture Model Feed Forward Neural Network (GMM-FFNN) architectures showed an accuracy of 96.7% and 96.42% with F1 scores of 0.9649, 0.9604 respectively.

Abstract Image

基于语音清晰度的肢体瘫痪严重程度分类:混合方法
语音清晰度是评估构音障碍严重程度的主要因素,构音障碍是一种语言障碍,其原因不仅在于发声运动肌无力,还在于难以控制发声运动。在康复过程中,有关构音障碍严重程度的先验信息可为治疗师提供帮助。本文介绍了一种由高斯混合模型和神经网络(GMM-NN)组成的新型混合架构,可根据语音清晰度将构音障碍的严重程度分为四个等级。从分段语音信号中提取的 Mel Frequency Cepstral Coefficients (MFCC) 用于训练分类器。与基线高斯混杂模型(GMM)相比,拟议模型的准确率提高了 1.9%。高斯混杂模型深度神经网络(GMM-DNN)和高斯混杂模型前馈神经网络(GMM-FFNN)架构的准确率分别为 96.7% 和 96.42%,F1 分数分别为 0.9649 和 0.9604。
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来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
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