Coupling of Bottom-Up and Top-Down Knowledge Sources for Automatic Speech Recognition

这项拨款将支持几种声学-语音信息编码技术的研究，这些技术将用于不依赖说话人的连续语音识别。时延和递归神经网络架构将用于重叠语音片段的频谱分析，并与语音频谱动态的贝叶斯最大似然分类进行比较。然后，神经网络输出将被编码为近似的语音距离，并使用两级迭代训练方法来优化整体识别。本文还将研究该系统与隐马尔可夫语音识别的集成。

This grant will support research in several techniques for encoding acoustic-phonetic information to be used in speaker- independent continuous-speech recognition. Time-delay and recursive neural network architectures will be used for spectral analysis of overlapping speech segments, to be compared with a Bayesian maximum-likelihood classification of the speech spectral dynamics. Neural-network outputs will then be encoded as approximate phonetic distances, with a two-level iterative training method to optimize overall recognition. Integration of this system with hidden-Markov speech recognition will also be studied.