Robust Speech Recognition Using Vector Computing

该项目正在探索语音算法在Torrent处理器上的矢量实现，该处理器是在以前的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，正在被矢量化并在Torrent处理器上实现。该系统将使用一个2000字的自然语音识别任务进行演示，该任务将使用一个独立的麦克风在嘈杂的环境中运行。

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.