Large-vocabulary continuous speech recognition on spontaneous speech task

1.在大词汇量连续语音识别中，我们研究了几种声学和语言模型的无监督自适应方法，并在自发日语语料库（CSJ）上对这些方法进行了评估。LVCSR系统具有全协方差矩阵作为声学模型。识别实验结果表明，通过对自适应数据按词性加权，自适应算法的误识率从未自适应的19.17%降低到无监督自适应的14.73%，进而降低到无监督自适应的14.47%。在CSJ上比较了连续混合弗拉姆（CHMM）系统和离散混合HMM（DMHMM）系统的性能。结果表明，DMHMM系统提供了几乎与CHMM系统相同的性能，并且在6000状态24混合DMHMM下获得了19.73%的WER，尽管人们普遍认为DMHMM的识别错误率是 关于我们 远远高于CHMM的水平.基于离散混合隐马尔可夫模型的鲁棒语音识别本文提出了一种基于离散混合隐马尔可夫模型（DMHMM）的噪声环境下鲁棒语音识别方法。DMHHT最初被提出来减少解码过程中的计算成本。最近，我们已经将DMHHT应用于含噪语音识别，并发现它们对含噪语音建模是有效的。为了进一步提高语音识别的抗噪性，提出了一种基于直方图均衡化（HEQ）的DMH归一化方法。HEQ方法可以补偿加性噪声的非线性效应。它通常用于连续混合HMM（CHMM）系统的特征空间归一化。在本文中，我们提出了两个模型空间和特征空间规范化的DMHQs使用HEQ。在模型空间归一化中，利用HEQ方法推导出的变换函数对DMH码本进行修正。所提出的方法进行了比较，使用传统的CHCl 3和DMHCl 3。实验结果表明，采用多个变换函数对DMH进行模型空间归一化，对噪声鲁棒性语音识别是有效的。少

1. Large-vocabulary continuous speech recognition on spontaneous speech taskIn large-vocabulary continuous speech recognition, we investigate several methods of unsupervised adaptation of both acoustic and language models and evaluate the methods on the Corpus of Spontaneous Japanese (CSJ). The LVCSR system has full-covariance matrices as the acoustic model. The results of recognition experiments showed the decrease in word error rate (WER) from 19.17% without adaptation to 14.73% with unsupervised adaptation, moreover to 14.47% with unsupervised adaptation by weighting the adaptation data on the basis of a part of speech. Also, we compared the performance between continuous-mixture FRAM (CHMM) system and discrete-mixture HMM (DMHMM) system on the CSJ. As a result, DMHMM system provided almost the same performance as the CHMM system and WER of 19.73% had been obtained with 6000-state 24-mixture DMHMMs, though it has been generally believed that the recognition error rates of DMHMM were … More much higher than those of CHMM until now.2. Robust speech recognition using discrete-mixture HMMsWe introduce a new method of robust speech recognition under noisy conditions based on discrete-mixture HMMs (DMHMMs). DMHMMs were originally proposed to reduce calculation costs in the decoding process. Recently, we have applied DMHMMs to noisy speech recognition, and found that they were effective for modeling noisy speech. Towards the further improvement of noise-robust speech recognition, we propose a novel normalization method for DMHMMs based on histogram equalization (HEQ). The HEQ method can compensate the nonlinear effects of additive noise. It is generally used for the feature space normalization of continuous-mixture HMM (CHMM) systems. In this paper, we propose both model space and feature space normalization of DMHMMs by using HEQ. In the model space normalization, codebooks of DMHMMs are modified by the transform function derived from the HEQ method. The proposed method was compared using both conventional CHMMs and DMHMMs. The results showed that the model space normalization of DMHMMs by multiple transform functions was effective for noise-robust speech recognition. Less