RI: Medium: Collaborative Research: The Effect of Subglottal Resonances on Machine and Human Speaker Normalization

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。尽管不同说话者的语音存在很大的声音差异，但人类通常能够快速而轻松地相互理解。人类将这种变异性映射到一组音素上的机制已经研究了50多年。这个“说话人归一化”问题通常被认为是用一组参考的共振峰来归一化特定说话人的共振峰频率。在这个项目中，我们探索了一种新的说话人归一化方法，不是对共振峰进行归一化，而是对声门下共振(SGR)进行归一化。以前已经证明，SGR定义了一组频带，在这些频带内，共振峰可以变化，但保持了相同的音素元音质量。因此，将SGR(及其相关频段)归一化可以有效地降低共振峰的可变性。在这个项目中，评估了SGR归一化对说英语和西班牙语的成人和儿童的自动语音识别(ASR)性能的影响。同时，还探讨了多个说话人环境下对人类语音感知的影响。结果有望改善ASR的性能，并揭示人类的语音产生和感知。该项目将产生语音数据库(包括SGR声学的直接记录)和ASR工具，这些工具对于语音产生、感知、说话人识别以及用于人工耳蜗植入和多语言ASR的语音处理算法的研究至关重要。工程学、语言学、言语与听觉和心理学方面的合作促进了一个多学科的学习环境。出版物、结果、数据库和工具将分发给研究社区。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).Despite large acoustic differences in the speech of various talkers, humans are generally able to understand each other quickly and easily. The mechanisms by which humans map such variability onto a set of phonemes has been the subject of research for more than 50 years. This "speaker normalization" problem has generally been thought of in terms of normalizing the formant frequencies of a particular speaker with a reference set of formants. In this project, a novel approach to speaker normalization is explored, in which not formants but subglottal resonances(SGRs) are normalized. SGRs have previously been shown to define a set of frequency bands within which formants may vary, yet retaining the same phonemic vowel quality. Normalizing SGRs (and associated frequency bands) therefore reduces formant variability in an effective way. In this project, effects of SGR normalization on automatic speech recognition(ASR) performance are evaluated for both adult and child speakers of English and Spanish. In parallel, effects on human speech perception in multi-talker conditions are explored. Results are expected to improve ASR performance and shed light on human speech production and perception. The project will result in speech databases (including direct recordings of SGR acoustics) and ASR tools, which are critically useful for research in speech production, perception, speaker identification, and speech processing algorithms for cochlear implants and multi-lingual ASR. The collaboration in Engineering, Linguistics, Speech & Hearing, and Psychology facilitates a multidisciplinary learning environment.Publications, results, databases, and tools will be disseminated to the research community.