SGER: Exploration of a Neurological Model to Improve the Extraction of Linguistic Features in Speech

在国家科学基金会的支持下，Carol Espy-Wilson博士将研究基于知识的语音识别系统在噪声中的性能。本研究从语言学角度出发，寻求语言特征的声学关联。该项目强调PI更大研究议程的两个组成部分：(1)分析信号表示以确保其鲁棒性；(2)应用神经模型在提取基于知识的语音信号表示之前或期间提高信噪比。该项目更广泛的影响在于它在独立于说话人的语音识别方面取得的进展，这在工业、教育和语言康复方面都有实际应用。

With National Science Foundation support, Dr. Carol Espy-Wilson will study a knowledge-based speech recognition system's performance in noise. The research is linguistically motivated way, in that it seeks acoustic correlates of linguistic features. This project emphasizes two components of the PI's larger research agenda: (1) analysis of signal representation to ensure its robustness, and (2) application of a neural model to enhance the signal-to-noise ratio before or during the extraction of a knowledge-based speech signal representation. The broader impacts of the project lie in its progress towards speaker-independent speech recognition, which has practical applications in industry, education and speech rehabilitation.