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

Purpose: 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.Conclusion: 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.

目的：随着可获取的语音数据和类型日益增多，医疗保健和科技行业对语音内容定量洞察的需求亦日益增长。语音数据在揭示认知、情感和心理健康状况以及行为方面的潜力，关键取决于将语音数据融入科学流程的能力。当前获取、分析和建模语音数据的工程技术方法，为将语音数据融入科学流程提供了机遇。此外，机器学习系统能够识别数据中的模式，从而有助于假设生成、数据分析以及统计分析。本文旨在（a）回顾跨这些领域的发展，这些发展使得实时磁共振成像能够揭示异常语音发音的各个方面；（b）在相同脉络下，讨论信号处理技术的进步如何有助于加深对自闭症谱系障碍相关沟通标志的理解；（c）强调将这些技术进步应用于这些领域的临床意义和影响。结论：工程师、语音科学家和临床医生的合作成果包括（a）开发出已被证明对小型和大型分析都极具价值的生物启发式技术，（b）深化了对典型和受损语音产生的实践与理论理解，（c）建立和提升了诊断和治疗工具，这一切都具有深远的多学科意义。