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Development of Noise Robust Speech Recognition and Its Application on Mobile Environment

噪声鲁棒语音识别的发展及其在移动环境下的应用

基本信息

批准号：
16500097
负责人：
KOSAKA Tetsuo
金额：
$ 1.86万
依托单位：
Yamagata University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

项目摘要

1) Noisy speech recognition using DMHMMsWe have proposed new methods of robust speech recognition using discrete-mixture HMMs (DMHMMs). The aim of this work is to develop robust speech recognition for adverse conditions that contain both stationary and non-stationary noise. In particular, we focus on the issue of impulsive noise, which is a major problem in practical speech recognition system. In order to solve the problem, we have proposed two methods. First, an estimation method of DMHMM parameters based on MAP has been proposed aiming to improve trainability. The second is a method of compensating the observation probabilities of DMHMMs by threshold to reduce adverse effect of outlier values. Experimental evaluations on Japanese LVCSR for read newspaper speech showed that the proposed method achieved the average error rate reduction of 28.1% in adverse conditions that contain both stationary and impulsive noises.2) Model Based Histogram Equalization for Noise Robust Speech Recognition by Using DMHMMsTowards further improvement of noisy speech recognition, we have proposed a novel normalization method for codebooks of DMHMMs in this paper. The codebook normalization method is based on histogram equalization (HEQ) and it can compensate the non-linear effects of additive noise in model space. The proposed method was compared with both conventional continuous-mixture HMMs (CHMMs) and DMHMMs. It showed that the proposed method obtained the best performance, and obtained an average relative improvement of 29.2% over the CHMM baseline.

1)基于DMHMM的噪声语音识别我们提出了一种基于离散混合HMM（DMHMM）的鲁棒语音识别方法。这项工作的目的是发展强大的语音识别的不利条件下，包含平稳和非平稳噪声。特别是，我们专注于脉冲噪声的问题，这是一个主要的问题，在实际的语音识别系统。为了解决这个问题，我们提出了两种方法。首先，提出了一种基于MAP的DMHMM参数估计方法，旨在提高可训练性。第二种方法是通过阈值补偿DMH的观测概率，以减少离群值的不利影响。实验结果表明，该方法在含有平稳噪声和脉冲噪声的恶劣环境下，平均错误率降低了28.1%。2）基于模型的直方图均衡的DMHMM抗噪语音识别为了进一步提高含噪语音识别的性能，本文提出了一种新的DMHMM码本归一化方法。码书归一化方法是基于直方图均衡（HEQ），它可以补偿模型空间中的加性噪声的非线性影响。将所提出的方法与传统的连续混合隐马尔可夫模型（CHMM）和DMHMM进行了比较。结果表明，该方法获得了最好的性能，并获得了平均相对改善29.2%的CHMM基线。