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.

在美国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。这项研究是以语言为动机的，因为它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稳健性，以及 (2) 应用神经模型在知识提取之前或期间提高信噪比基于语音信号的表示。该项目更广泛的影响在于其在与说话者无关的语音识别方面取得的进展，该识别在工业、教育和言语康复方面都有实际应用。