Engineering In Speech Science (Hagedorn et al., 2019)

<b>Purpose:</b> As increasing amounts and types of speech data become accessible, health care and technology industries increasingly demand quantitative insight into speech content.The potential for speech data to provide insight into cognitive, affective, and psychological health states and behavior crucially depends on the ability to integrate speech data into the scientific process. Current engineering methods for acquiring, analyzing, and modeling speech data present the opportunity to integrate speech data into the scientific process. Additionally, machine learning systems recognize patterns in data that can facilitate hypothesis generation, data analysis, and statistical modeling. The goals of the present article are (a) to review developments across these domains that have allowed real-time magnetic resonance imaging to shed light on aspects of atypical speech articulation; (b) in a parallel vein, to discuss how advancements in signal processing have allowed for an improved understanding of communication markers associated with autism spectrum disorder; and (c) to highlight the clinical significance and implications of the application of these technological advancements to each of these areas.<br><b>Conclusion:</b> The collaboration of engineers, speech scientists, and clinicians has resulted in (a) the development of biologically inspired technology that has been proven useful for both small- and large-scale analyses, (b) a deepened practical and theoretical understanding of both typical and impaired speech production, and (c) the establishment and enhancement of diagnostic and therapeutic tools, all having far-reaching, interdisciplinary significance.

<b>研究目的：</b> 随着语音数据的规模与类型持续拓展，医疗健康与科技产业对语音内容的量化分析洞察需求日益增长。语音数据在认知、情感与心理健康状态及行为维度的潜在应用价值，核心取决于其能否融入科学研究流程。当前用于获取、分析与建模语音数据的工程技术手段，为将语音数据整合进科学研究流程提供了契机。此外，机器学习系统可识别数据内的模式，助力假设生成、数据分析与统计建模工作。本文的研究目标包含三项：(a) 梳理各相关领域的技术进展，这些进展借助实时磁共振成像（real-time magnetic resonance imaging）揭示了非典型语音发音的相关特征；(b) 同步探讨信号处理领域的技术进步如何提升了对自闭症谱系障碍（autism spectrum disorder）相关沟通标志物的认知水平；(c) 阐明将上述技术进展应用于上述两大领域的临床意义与实践价值。<br><b>研究结论：</b> 工程师、语音科学家与临床医师的跨领域协作，已取得以下成果：(a) 研发出受生物学原理启发的技术，经证实可适用于小规模与大规模分析场景；(b) 深化了对典型与非典型语音产生机制的实践与理论认知；(c) 建立并优化了诊断与治疗工具，所有这些成果均具备深远的跨学科意义。