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

SGER：探索神经模型以改进语音中语言特征的提取

• 批准号: 0233482
• 负责人: Carol Espy-Wilson
• 金额: $ 5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2004-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0233482&HistoricalAwards=false
• 关键词: SGER; Exploration; Neurological; Model; Improve

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.

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。本研究是从语言学的角度出发，寻找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，以及（2）在提取基于知识的语音信号表示之前或期间应用神经模型来提高信噪比。该项目的更广泛影响在于其在非特定人语音识别方面的进展，该语音识别在工业，教育和语音康复方面具有实际应用。