Speech Technology for Automatic Recognition and Assessment of Dysarthric Speech: An Overview.

IF 2.2 2区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Chitralekha Bhat, Helmer Strik
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引用次数: 0

Abstract

Purpose: In this review article, we present an extensive overview of recent developments in the area of dysarthric speech research. One of the key objectives of speech technology research is to improve the quality of life of its users, as evidenced by the focus of current research trends on creating inclusive conversational interfaces that cater to pathological speech, out of which dysarthric speech is an important example. Applications of speech technology research for dysarthric speech demand a clear understanding of the acoustics of dysarthric speech as well as of speech technologies, including machine learning and deep neural networks for speech processing.

Method: We review studies pertaining to speech technology and dysarthric speech. Specifically, we discuss dysarthric speech corpora, acoustic analysis, intelligibility assessment, and automatic speech recognition. We also delve into deep learning approaches for automatic assessment and recognition of dysarthric speech. Ethics committee or institutional review board did not apply to this study.

Conclusions: Overcoming the challenge of limited data and exploring new avenues in data collection, artificial intelligence-powered analysis and teletherapy hold immense potential for significant advancements in dysarthria research. To make longer and faster strides, researchers typically rely on existing research and data on a global scale. Therefore, it is imperative to consolidate the existing research and present it in a form that can serve as a basis for future work. In this review article, we have reviewed the contributions of speech technologists to the area of dysarthric speech with a focus on acoustic analysis, speech features, and techniques used. By focusing on the existing research and future directions, researchers can develop more effective tools and interventions to improve communication, quality of life, and overall well-being for people with dysarthria.

语言障碍语音自动识别与评估的语音技术综述
目的:在这篇综述性文章中,我们对困难言语研究领域的最新进展进行了广泛的概述。语音技术研究的关键目标之一是提高用户的生活质量,这一点可以从当前研究趋势的重点上得到证明,即创建包容性的会话界面,以迎合病态语音,其中语言障碍是一个重要的例子。语音技术研究在困难语音中的应用需要清楚地了解困难语音的声学以及语音技术,包括用于语音处理的机器学习和深度神经网络。方法:我们回顾有关言语技术和言语困难的研究。具体来说,我们讨论了语言障碍语料库、声学分析、可理解性评估和自动语音识别。我们还深入研究了深度学习方法来自动评估和识别诵读困难的语音。伦理委员会或机构审查委员会未申请本研究。结论:克服有限数据的挑战,探索数据收集的新途径,人工智能驱动的分析和远程治疗在构音障碍研究中具有巨大的潜力。为了取得更长、更快的进展,研究人员通常依赖于全球范围内的现有研究和数据。因此,当务之急是巩固现有的研究,并以一种可以作为未来工作基础的形式提出。在这篇综述文章中,我们回顾了语音技术专家对困难语音领域的贡献,重点是声学分析、语音特征和使用的技术。通过关注现有的研究和未来的方向,研究人员可以开发出更有效的工具和干预措施,以改善构音障碍患者的沟通、生活质量和整体福祉。
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来源期刊
Journal of Speech Language and Hearing Research
Journal of Speech Language and Hearing Research AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY-REHABILITATION
CiteScore
4.10
自引率
19.20%
发文量
538
审稿时长
4-8 weeks
期刊介绍: Mission: JSLHR publishes peer-reviewed research and other scholarly articles on the normal and disordered processes in speech, language, hearing, and related areas such as cognition, oral-motor function, and swallowing. The journal is an international outlet for both basic research on communication processes and clinical research pertaining to screening, diagnosis, and management of communication disorders as well as the etiologies and characteristics of these disorders. JSLHR seeks to advance evidence-based practice by disseminating the results of new studies as well as providing a forum for critical reviews and meta-analyses of previously published work. Scope: The broad field of communication sciences and disorders, including speech production and perception; anatomy and physiology of speech and voice; genetics, biomechanics, and other basic sciences pertaining to human communication; mastication and swallowing; speech disorders; voice disorders; development of speech, language, or hearing in children; normal language processes; language disorders; disorders of hearing and balance; psychoacoustics; and anatomy and physiology of hearing.
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