STUDIES OF AN ADVANCED AUDITORY MODEL AND THE APPLICATION TO IMPROVE THE ROBUSTNESS OF CONTINUOUS SPEECH RECOGNITION

我们的最终目标是开发一个可靠的连续语音识别系统的基础上，人类听觉系统的模型。（1）在已有的以离散隐马尔可夫模型（DHM）为识别工具的基于子词单元的孤立词识别器（VQ-SWR）的基础上，研究了提高识别器对说话人和环境噪声的鲁棒性。实验结果表明：[1]提出了一种用半连续H_2代替DH_2的识别器。实验结果表明，新的识别器在非特定人识别方面有很大的提高。[2]本文在VQ-SWR的基础上，提出了一种新的基于子词单元的孤立词识别器，它包括一个多参与者和一个说话人自适应步骤.它由DHQ和一个学习矢量量化器（LVQ）组成，LVQ中包含了从VQ-SWR中获得的输入子词分类信息的反馈.在VQ-SWR的基础上，我们提出了一种新的基于子词单元的孤立词识别器，它包括一个多参与者和一个说话人自适应步骤. ...更多信息 实验结果表明，新的语音识别器在稳态下对说话人和噪声的鲁棒性等方面都优于传统的语音识别器VQ-SWR。(2)为了获得比VQ-SWR更高的单词识别率和更高的噪声鲁棒性，我们提出了一种新的识别器（CM-RN-SWR），该识别器由称为“耳蜗非线性反馈模型”的人类耳蜗模型（NLF-COM）、具有自循环类型反馈连接的简单多层递归神经网络（RNN）和单词的DHNN组成。我们开发的NLF-COM和RNN已分别作为人类听觉系统的模型，语音频谱分析器和子词识别器。实验结果表明，识别准确率为干净的语音和语音在伪白噪声的存在下，大大提高了说话人相关的应用相比，VQ-SWR。少

Our final goal is to develop a reliable continuous speech recognition system based on a model of human auditory system. So, we have studied as follows :(1) On the base of a subword-unit-based isolated word recognizer (VQ-SWR) with the discrete hidden Markov models (DHMMs) as a recognition tool, which we developed before, the research to improve the robustness for speakers and some environment noises have been done. As experimental results, findings can be summarized as follows :[1] A new recognizer with the DHMMs replaced with the semi-continuous HMMs have been developed. Experimental results showed a considerable improvement of the new recognizer in speakerindependency.[2] We have developed a new subword-unit-based isolated word recognizer incorporated a multiparty and a speaker adaptation step on the base of the VQ-SWR.This is made up of DHMMs and a learning vector quantizer (LVQ) incorporated a feedback of information on the classification of input subword which is obtained from the … More output of the LVQ.Experimental results showed that the new recognizer performance including the robustness for speaker and noise in stationary states is higher than those accomplished with the conventional recognizer VQ-SWR.(2) To aim at achieving higher word recognition rates and higher noise robustness than the VQ-SWR, we have proposed a new recognizer (CM-RN-SWR) made up of a model (NLF-COM) of human cochlea called "a nonlinear feedback model for cochlea", a simple multi-layer recurrent neural network (RNN) which has feedback connections of self-loop type, and DHMMs for words. The NLF-COM and the RNN which were developed before by us has been used as a model of the human auditory system, and as a kind of spectrum analyzer for speech sounds and a subword recognizer, respectively. Experimental results showed that recognition accuracies for clean speech and speech in the presence of pseud-white noise are considerably improved in speaker-dependent applications in comparison with the VQ-SWR. Less