Robust Speech Recognition Using Vector Computing

使用矢量计算的鲁棒语音识别

• 批准号: 9612778
• 负责人: Nelson Morgan
• 金额: $ 28.51万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-01-15 至 1998-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9612778&HistoricalAwards=false
• 关键词: Robust; Speech; Recognition; Using; Vector

This project is exploring the vector implementation of speech algorithms on the torrent processor, which was developed under previous NSF support. Torrent is specialized for the low-precision digital signal processing that arises in speech recognition; it combines multiple fixed-point datapaths, a high bandwidth memory system, and a high-speed RISC general-purpose controller. Algorithms for the computationally intensive tasks of speech recognition, including acoustic probability estimation, feature extraction, lexical search, and evaluation of grammatical probabilities, are being vectorized and implemented on Torrent processor. The system will be demonstrated using a 2000-word natural speech recognition task that will run using a free-standing microphone in a noisy environment.

这个项目正在探索语音算法在种子处理器上的矢量实现，这是在以前的NSF支持下开发的。Torrent专门用于语音识别中出现的低精度数字信号处理；它结合了多个定点数据路径、高带宽存储系统和高速RISC通用控制器。语音识别的计算密集型任务的算法，包括声学概率估计、特征提取、词汇搜索和语法概率评估，正在矢量化并在Torrent处理器上实现。该系统将使用一个在嘈杂环境中使用独立麦克风运行的2000字自然语音识别任务进行演示。