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学差异，但人类通常能够快速轻松地相互理解。人类将这种变异性映射到一组音素上的机制已经是50多年来的研究主题。这个“说话者归一化”问题通常被认为是用共振峰的参考集合来归一化特定说话者的共振峰频率。 在这个项目中，一个新的方法来说话人归一化进行了探索，其中没有共振峰，但声门下共振（SGR）进行归一化。SGR以前已经被证明定义一组频带，在这些频带内共振峰可能会发生变化，但保留相同的音素元音质量。因此，归一化SGR（和相关联的频带）以有效的方式减少共振峰可变性。在这个项目中，SGR标准化对自动语音识别（ASR）性能的影响进行了评估，为成人和儿童的英语和西班牙语的扬声器。同时，探讨了在多说话者条件下对人类语音感知的影响。预计结果将提高ASR性能，并阐明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，这些工具对语音产生，感知，说话人识别以及人工耳蜗和多语言ASR的语音处理算法的研究非常有用。在工程学、语言学、言语听力和心理学方面的合作促进了多学科的学习环境。出版物、成果、数据库和工具将传播给研究界。

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.