Voleti Rohit, Liss Julie M, Berisha Visar
School of Electrical, Computer, & Energy Engineering, Arizona State University, Tempe, AZ, 85281 USA.
IEEE J Sel Top Signal Process. 2020 Feb;14(2):282-298. doi: 10.1109/jstsp.2019.2952087. Epub 2019 Nov 7.
It is widely accepted that information derived from analyzing speech (the acoustic signal) and language production (words and sentences) serves as a useful window into the health of an individual's cognitive ability. In fact, most neuropsychological testing batteries have a component related to speech and language where clinicians elicit speech from patients for subjective evaluation across a broad set of dimensions. With advances in speech signal processing and natural language processing, there has been recent interest in developing tools to detect more subtle changes in cognitive-linguistic function. This work relies on extracting a set of features from recorded and transcribed speech for objective assessments of speech and language, early diagnosis of neurological disease, and tracking of disease after diagnosis. With an emphasis on cognitive and thought disorders, in this paper we provide a review of existing speech and language features used in this domain, discuss their clinical application, and highlight their advantages and disadvantages. Broadly speaking, the review is split into two categories: language features based on natural language processing and speech features based on speech signal processing. Within each category, we consider features that aim to measure complementary dimensions of cognitive-linguistics, including language diversity, syntactic complexity, semantic coherence, and timing. We conclude the review with a proposal of new research directions to further advance the field.
人们普遍认为，通过分析语音（声学信号）和语言生成（单词和句子）所获得的信息是洞察个体认知能力健康状况的一个有用窗口。事实上，大多数神经心理学测试组合都有一个与语音和语言相关的部分，临床医生会从患者那里引出语音，以便在广泛的维度上进行主观评估。随着语音信号处理和自然语言处理技术的进步，最近人们对开发能够检测认知语言功能中更细微变化的工具产生了兴趣。这项工作依赖于从录制和转录的语音中提取一组特征，用于语音和语言的客观评估、神经系统疾病的早期诊断以及诊断后疾病的跟踪。本文重点关注认知和思维障碍，对该领域现有的语音和语言特征进行了综述，讨论了它们的临床应用，并突出了它们的优缺点。广义地说，该综述分为两类：基于自然语言处理的语言特征和基于语音信号处理的语音特征。在每一类中，我们考虑旨在测量认知语言学互补维度的特征，包括语言多样性、句法复杂性、语义连贯性和时间性。我们在综述结尾提出了新的研究方向建议，以进一步推动该领域的发展。
