基于PCANN/HMM混合结构的语音识别方法

本文提出了一种基于PCANN/HMM混合结构的语音识别方法,它采用相继几帧组成的特征参数矢量作为语音识别HMM的输入,能有效地在语音识别HMM中引入帧间相关信息,同时为了改善多帧特征输入HMM的输出概率密度函数性能,在HMM的前端增加语音参数压缩的主分量分析神经网络(PCANN).通过对多讲者汉语连续语音识别实验,证实了本文提出方法的有效性.     

语音识别HMM中引入帧间相关信息的一种参数化模型

虽然隐马尔可夫模型（ＨＭＭ）是当前最为流行的语音识别模型，但由于一般都采用了状态输出独立假设，因此存在着不能描述语音现象中时间相关性的固有缺陷，本文提出的新模型对语音状态输出特征矢量序列的静态和动态特性信息分别进行参数化建模，然后将它们结合在一起，由此在基于段长分布的ＨＭＭ（ＤＤＢＨＭＭ）中引入了帧间相关信息，这种上引入帧间相关信息的ＨＭＭ能够更为精确地描述真实的语音现象。本文在给出新模型的框架后     

基于概率DP匹配法的噪声环境下说话人识别方法的研究

提出了一种在噪声环境下利用复数语音帧段主分量特征输入的概率DP匹配的说话人识别算法。通过对噪吉环境下与文本无关的说话人识别实验，验证了这种方法的有效性。     

基于高阶统计处理技术的m-序列帧同步码识别

该文针对保密通信体制下帧同步码识别问题,在对m-序列采样后的三阶相关函数(TCF)和偏三阶相关函数进行研究的基础上,首次提出了基于偏三阶相关函数峰值特性的识别方法分析和判断帧长。该方法首先对同步码各位上的序列进行偏三阶相关函数运算,然后利用m-序列采样后的偏三阶相关函数峰值相似度高的特性区分信息码和同步码,最后通过进一步运算得到同步码的码长和起始位。仿真实验表明:该方法能够利用较少的数据快速准确地识别出帧同步码,并具有良好的容错性能。     

基于相关检测方法的一种帧同步系统

提出将相关检测方法应用于一种帧同步系统,介绍了该系统的以DSP(数字信号处理)专用芯片为核心的终端机硬件结构,引入了数据检测和帧同步码识别的数学模型,采用TMS320C50软件实现对数据的检测、判决及对帧同步码的识别,并给出实验结果。     

基于跟踪共振峰的语音增强算法

该文通过实验方法研究和分析了汉字语音共振峰的特点,发现可跟踪并找到各个共振峰,结合汉字发音所具有的一般规律,提出了一种基于跟踪共振峰的语音增强算法。该算法能够有效地识别出带噪语音中的语音帧和非语音帧,简单且有效地去除非语音帧的全部噪声,明显抑制语音帧内的噪声。算法计算复杂度低并具有噪声环境可移植性。     

改进的降低空间分辨率视频编码转换算法

该文提出一种改进的降低空间分辨率视频编码转换算法。根据漂移误差与帧间编码块的运动活动性的关系,提出了一种自适应帧内刷新方法。该方法根据目标比特率和实际比特率的差值,动态地调整阈值,从而有效地减少了帧内编码的比特率,保持了对差错的鲁棒性,限制了差错的时间传播。同时,将率失真函数映射为线性函数,以较少计算复杂度,提出了一种线性速率控制策略。仿真结果表明:该文提出的方法有效地平滑了缓冲器的输出,同时峰值信噪比也有所提高。     

从线性预测HMM到一种新的语音识别的混合模型

线性预测HMM(Linear Prediction HMM,LPHMM)并没有象传统HMM那样引入状态输出独立同分布假设,但实用中识别性能并不佳.通过分析两种HMM的各自优劣,本文提出了一种新的语音识别的混合模型,将语音静态特性(基于传统HMM)和动态特性(基于LPHMM)分别描述又有机结合在一起,更为精确地刻划了真实的语音现象,同时又继承使系统的实现改动很小和较小的计算量.汉语大词汇量非特定人连续语音识别的实验表明,混合模型的识别性能显著好于LPHMM和传统HMM.理论上,本文还给出了LPHMM的一组闭式参数重估公式.     

基于HMM状态聚类均值替代的宽带语音ISF参数补偿算法

宽带语音在Internet传输中不可避免会出现丢帧现象,由于错误传播的影响,使接收语音质量急剧下降。该文采取大型连续分布隐马尔可夫模型(LCDHMM)对宽带语音ISF参数建模,采用Viterbi算法确定丢失帧之前若干语音帧ISF参数观察值的最佳状态序列。由于状态的冗余度较大,用丢帧前最近接收的正确帧ISF参数的HMM状态对应的聚类均值和真实值的加权,代替丢失帧的ISF参数值。将采取该算法的补偿语音和采取G.722.2标准附件I所提算法的补偿语音进行比较,仿真结果表明该算法具有较好的补偿效果,其波形与谱失真更小。     

基于分段模糊聚类算法的VQ-HMM语音识别模型参数估计

提出了一种新的语音识别模型VQ－HMM。它综合VQ和HMM的优点，在每个状态通过用矢量量化误差值取代传统HMM的输出概率值来建立模型，同时提出了基于分段模糊聚类算法的模型参数估计方法。文中给出了模型的实现方法，并通过实验证明了其在语音识别中具有很好的性能。     

Speech recognition algorithm based on neural network and hidden Markov model

This study proposes a hybrid model of speech recognition parallel algorithm based on hidden Markov model (HMM) and artificial neural network (ANN). First, the algorithm uses HMM for time-series modeling of speech signals and calculates the voice to the HMM of the output probability score. Second, with the probability score as input to the neural network, the algorithm gets information for classification and recognition and makes a decision based on the hybrid model. Finally, Matlab software is used to train and test sample data. Simulation results show that using the strong time-series modeling ability of HMM and the classification features of neural network, the proposed algorithm possesses stronger noise immunity than the traditional HMM. Moreover, the hybrid model enhances the individual flaws of the HMM and the neural network and greatly improves the speed and performance of speech recognition.     

Addable Stress Speech Recognition with Multiplexing HMM: Training and Non-training Decision

In stress speech recognition, a recognition model that is capable of processing multi-stress speech needs to be designed in the view points of accuracy and add-ability. This paper proposes addable stress speech recognition with multiplexing Hidden-Markov model (HMM). To achieve multi-stress speech, we propose a multiplexing topology that combines multiple stress speech models. Since each stress affects a speech in different way, having a speech recognition model that specifically trained to recognize words effected by the stress help improve the recognition rates. However, since each stress speech model gives it own independent recognized word, we need to have an effective decision module to choose the correct word. In each stress speech model, a MFCC is applied to the input speech. The result is fed into a HMM that is segmented into N parts. Each part of the segmentation provides its own tentative recognized word which in turn is an input to the proposed non-training decision module. Based on these tentative recognized words from segments of all stress speech models, the final recognized word is decided using coarse-to-fine concept performed by a majority vote, segment-weighted difference square score and next best score, respectively. Besides neutral speech, the proposed method was verified using three stresses including angry, loud, and Lombard. The results showed that the proposed method achieved 94.7 % recognition rate comparing to 94.2 % of the training-based decision method.     

Use of multiple vector quantisation for semicontinuous-HMM speechrecognition

Although the continuous hidden Markov model (CHMM) technique seems to be the most flexible and complete tool for speech modelling. It is not always used for the implementation of speech recognition systems because of several problems related to training and computational complexity. Thus, other simpler types of HMMs, such as discrete (DHMM) or semicontinuous (SCHMM) models, are commonly utilised with very acceptable results. Also, the superiority of continuous models over these types of HMMs is not clear. The authors' group has previously introduced the multiple vector quantisation (MVQ) technique, the main feature of which is the use of one separated VQ codebook for each recognition unit. The MVQ technique applied to DHMM models generates a new HMM modelling (basic MVQ models) that allows incorporation into the recognition dynamics of the input sequence information wasted by the discrete models in the VQ process. The authors propose a new variant of HMM models that arises from the idea of applying MVQ to SCHMM models. These are SCMVQ-HMM (semicontinuous multiple vector quantisation HMM) models that use one VQ codebook per recognition unit and several quantisation candidates for each input vector. It is shown that SCMVQ modelling is formally the closest one to CHMM, although requiring even less computation than SCHMMs. After studying several implementation issues of the MVQ technique. Such as which type of probability density function should be used, the authors show the superiority of SCMVQ models over other types of HMM models such as DHMMs, SCHMMs or the basic MVQs     

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% .     

有序聚类方法及其在神经网络语音识别中的应用

本文提出了一种新的网络结构,我们称之为有序聚类网络。这种网络能够对语音信号进行特征提取,很好地解决神经网络语音识别中的时间规整问题。有序聚类网络从输入语音信号的特征矢量序列中撮出一组固定数目的特 矢量,然后将这组特征矢量馈入神经网络分类器进行识别。和其他的神经网络语音识别方法相比较,用这种网络进行前端处理,可以缩短后端神经网络分类器的训练和识别时间,简化经分类器的网络产高的识别率。根据该 们建立了     

语音识别隐马尔可夫模型的改进

由于在语音识别中被广泛应用的隐马尔可夫模型是一重马尔可夫模型，它不能充分地描述语音信号的时间相依性。虽然理论上可将ＨＭＭ扩展成多重马尔可夫模型，但由于所需运算量和存储量将成指数增长而使其难以应用。因此，本文提出一种新模型，它是由ＨＭＭ与一个能描述语音信号时间相依性的多维高斯密度函数相结合构成的。本文从理论上论证了新模型的合理性。对汉语不计声调的全部４０９个单音节的识别实验结果表明：新模型的识别率显     

基于段长分布的HMM语音识别模型

本文针对齐次HMM语音识别模型在使用段长信息时存在的缺陷,形式化地定义了一种适合语音信号描述的自左向右非齐次隐含马尔科夫模型,证明了这种模型的状态转移概率表示与状态段长表示的等效性,并在此基础上提出了基于段长分布的HMM模型(DDBHMM).非特定人连续语音实验结果表明,仅仅利用状态段长信息的DDBHMM语音识别模型比经典HMM模型的性能有了明显的提高(误识率降低了17.8%),展示了DDBHMM的良好的性能,为语音信号的时长、语速、时间断续性以及语音特征的相关性等重要特征的描述和利用开辟了空间.