基于环境特征判别学习的欷顽健语音识别方法

提出一种基于环境特征判别学习的顽健语音识别方法，它首先通过使用一个简单的分类器和梯度下降法迭代地学得环境特征，接首利用得到的环境特征从观测到的混噪音特征中估计出纯净的语音特征，然后将估计出来的纯净语音特征用到后端的HMM分类器中，使用所提出的方法对不特定者小词表进行实验，其系统误识率与基本HMM系统相比下降了33.3%。     

基于环境特征判别学习的顽健语音识别方法

提出一种基于环境特征判别学习的顽健语音识别方法 ,它首先通过使用一个简单的分类器和梯度下降法迭代地学得环境特征 ,接着利用得到的环境特征从观测到的混噪语音特征中估计出纯净的语音特征 ,然后将估计出来的纯净语音特征用到后端的HMM分类器中 .使用所提出的方法对不特定话者小词表进行实验 ,其系统误识率与基本HMM系统相比下降了 33 3% .     

噪声环境下顽健的语音识别系统

开发了一高噪声环境下特定人孤立词的语音识别系统，讨论了系统性能的考核情况，考核实验表明，系统在80dB以下噪声环境下工作，精度较高；在100dB的高噪声环境下，识别率高于96％，系统仍可使用。     

基于自适应共轭梯度参数估计的顽健卡尔曼滤波语音增强算法

对于加性噪声影响下的语音信号，利用双通道输入建立起来的增广卡尔曼滤波器模型，采用自适应共轭梯度方法对纯净语音和有色噪声干扰模型分别进行参数估计，提出了一种有效的语音增强算法。由于该方法对模型参数的估计精确性较高，而且估计速度快，同卡尔曼滤波类的其它语音增强方法相比，其语音增强效果良好，且具有一定的顽健性。仿真实验表明在环境噪声很复杂的情况下，该方法仍然有效。     

变异语音处理的研究进展

本文讨论了变异语音处理技术及其研究进展,分析了变异情况对语音识别性能产生的影响,综述了变异语音分类和变异语音识别方法,探讨了变异语音处理研究中存在的问题及未来的研究重点.     

基于超高斯激励的噪声顽健语音线性预测分析算法

针对传统的语音信号线性预测分析算法在噪声环境下性能恶化的问题,提出了一种新的基于超高斯激励的噪声顽健线性预测算法。该算法采用具有超高斯特性的学生t分布对语音信号线性预测激励建模,并显式地考虑环境噪声的影响,从而构建语音信号线性预测分析的概率图模型。在此基础上,利用变分贝叶斯的方法求解模型参数的近似后验分布,进而实现对带噪语音线性预测系数的最优估计。实验结果表明,该算法能够有效提高噪声环境下语音信号线性预测分析的顽健性。     

噪声下差分复合子带语音识别方法

本文根据子带特征反映语音信号局部特性和全带特征反映语音信号整体特性的事实，提出了 一种差分复合子带语音识别新方法。先用频谱差分减少噪声的干扰，再将多子带特征识别概率与全带特征识别概率相结合进行综合判决，以得到最终识别结果。将新方法应用于TIMIT数据包0－9十个英文数字和E－Set在NoiseX92的白噪声和F16战机噪声下的识别实验。实验结果表明新方法比传统方法识别性能有很大提高。     

利用DSP实现的实际环境下语音识别方法

该文提出了一种在实际环境下利用DSP实现的语音识别方案,通过户外实际环境的语音识别实验,这种方法的有效性得到了验证。     

基于HMM和LVQ网络混合模型的语音识别方法

提出一种基于隐马尔可夫模型(HMM)和学习向量量化(LVQ)神经网络的语音识别方法.该方法先用HMM生成最佳语音状态序列,然后用函数逼近技术产生对最佳状态序列进行时闻归正,最后通过LVQ神经网络进行分类识别.理论和实验结果表明,混合模型的识别率明显高于隐马尔可夫模型的识别率.     

Multi-model approach for noisy speech recognition

A multi-model approach for noisy speech recognition is proposed. This approach comprised an SVD-based preprocessing front-end and a multi-model HMM recognition structure. It can provide a high recognition rate over a large range of SNRs for speech recognition in wide-band additive noise     

Auditory model for robust speech recognition in real world noisyenvironments

A computationally efficient and noise-robust auditory model is developed based on the detection of zero-crossings for speech recognition in real world noisy environments     

Information-theoretic distortion measures for speech recognition

A wide variety of speech recognition distortion measures have been proposed and tested, including some especially effective ones. It is shown that there is a general framework, based on the concepts of information theory, linking most of these measures. The distortion measure between any two speech spectra can be defined in terms of the distortions between the associated probability distributions. This general framework defines three broad families of distortion measures for speech recognition and provides a consistent way of combining the energy and the spectral information of a phonetic event. In addition, the cepstral-domain representation for several distortion measures is derived, allowing comparison of these measures in a domain that also yields convenient equations for their practical implementation     

Subband-based blind signal separation for noisy speech recognition

A method for directly extracting clean speech features from noisy speech is proposed. This process is based on independent component analysis (ICA) and a new feature analysis technique for reducing the computational complexity of the frequency domain ICA. For noisy speech signals recorded in real environments, this method yielded a considerable performance improvement     

Nonlinear cepstral equalisation method for noisy speech recognition

The authors deal with the problem of automatic speech recognition in the presence of additive white noise. The effect of noise is modelled as an additive term to the power spectrum of the original clean speech. The cepstral coefficients of the noisy speech are then derived from this model. The reference cepstral vectors trained from clean speech are adapted to their appropriate noisy version to best fit the testing speech cepstral vector. The LPC coefficients, LPC derived cepstral coefficients, and the distance between test and reference, are all regarded as functions of the noise ratio (the spectral power ratio of noise to noisy speech). A gradient based algorithm is proposed to find the optimal noise ratio as well as the minimum distance between the test cepstral vector and the noise adapted reference. A recursive algorithm based on Levinson-Durbin recursion is proposed to simultaneously calculate the LPC coefficients and the derivatives of the LPC coefficients with respect to the noise ratio. The stability of the proposed adaptation algorithm is also addressed. Experiments on multispeaker (50 males and 50 females) isolated Mandarin digits recognition demonstrate remarkable performance improvements over noncompensated method under noisy environment. The results are also compared to the projection based approach, and experiments show that the proposed method is superior to the projection approach under a severe noisy environment     

一种用于抗噪语音识别的动态参数补偿新方法

模型补偿技术已成功应用到噪声环境下的语音识别任务中。流行的模型补偿技术如Log-Add和Log-Normal PMC（并行模型合并）方法对动态特征参数通常只能给出近似的补偿。因此他们的识别率在较低的信噪比条件下变得很低。本文利用静态特征的导函数推导出了一种新的动态模型参数补偿方法。新的方法可以同任何已知的静态模型补偿算法结合产生出新的用于识别的噪声语音模型。实验证明这一新算法的应用,使其识别率比仅使用原有的模型补偿算法有较为明显的提高,并且新算法的复杂度较原有的模型补偿算法只有轻微的增加。     

Improved perceptually non-uniform spectral compression for robust speech recognition

According to the decline of recognition rate of speech recognition system in the noise environments, an improved perceptually non-uniform spectral compression feature extraction algorithm is put forward in this paper. This method can realize an effective compression of the speech signals and make the training and recognition environments more matching, so the recognition rate can be improved in the noise environments. By experimenting on the intelligent wheelchair platform, the result shows that the algorithm can effectively enhance the robustness of speech recognition, and ensure the recognition rate in the noise environments.     

Mandarin speech recognition using segment-based cepstral comparisonin noisy conditions

A new scheme is proposed that compensates for the effects of noise in speech recognition systems. The new scheme was applied to Mandarin speech recognition. Another scheme, based on interpolation of the compensation vectors of several environments for a particular environment that is not obtained during the training phase, called interpolated SSDCN (ISSDCN), is also presented. Experimental results show that the scheme performs well under different SNR conditions     

鲁棒性语音识别技术研究综述

鲁棒性语音识别是为了解决噪声环境和混响环境等外界因素所引起的语音识别系统训练和识别不匹配的情况,针对在噪声和混响条件下进行鲁棒性语音识别的问题,对现有的鲁棒性语音识别研究进行了总结,阐述了语音识别的主要流程和整体框架;从特征提取和声学建模两个方面对语音增强技术、语音分离技术进行介绍;分析鲁棒性语音识别技术主要难点和实现过程。最后对鲁棒性语音识别技术进行总结和展望。