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语音识别隐马尔可夫模型的改进 总被引:7,自引:1,他引:6
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 相似文献
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QIN Wei WEI Gang 《中国电子科技》2006,4(1):43-46
As a kind of statistical method, the technique of Hidden Markov Model (HMM) is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model (SDCHMM), derived from the Continuous Density Hidden Markov Model (CDHMM), is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits. 相似文献
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作为汉语语音识别的重要组成部分,声调识别具有关键的作用.提出了一种新的基于前后文相关的模型识别方法用以提高汉语连续语音中的识别率.首先介绍用于声调识别的基因轨迹的提取和处理,然后提出6种特征来描述基因轨迹的变化趋势并给出具体的计算公式,利用这些特征并考虑连续语音中前后音节的相关性对基因轨迹造成的变化而建立细分的声调模型... 相似文献
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We present a discriminative training algorithm, that uses support vector machines (SVMs), to improve the classification of discrete and continuous output probability hidden Markov models (HMMs). The algorithm uses a set of maximum-likelihood (ML) trained HMM models as a baseline system, and an SVM training scheme to rescore the results of the baseline HMMs. It turns out that the rescoring model can be represented as an unnormalized HMM. We describe two algorithms for training the unnormalized HMM models for both the discrete and continuous cases. One of the algorithms results in a single set of unnormalized HMMs that can be used in the standard recognition procedure (the Viterbi recognizer), as if they were plain HMMs. We use a toy problem and an isolated noisy digit recognition task to compare our new method to standard ML training. Our experiments show that SVM rescoring of hidden Markov models typically reduces the error rate significantly compared to standard ML training. 相似文献
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本文针对老年人日常活动类型及特点提出了一种基于三轴加速度传感器和HMM(Hidden Markov Model)的活动识别方法.本文首先提取了针对老年人相异、相似活动的标准差、能量、相关系数、RAF(RAtio Forward)、RVF(Ratio Vertical Forward)等特征值.然后定义老年人的HMM活动识别模型.最后在经过Baum-Welch算法对HMM进行参数训练后使用Viterbi算法来进行老年人活动识别.实验结果表明,本文方法适用于老年人的日常活动的识别,平均识别精度达到了93.3%,尤其是对于相似步态活动的识别准确率达到了93.7%. 相似文献
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非接触式手势控制交互识别作为一种人机交互的新型技术,摆脱了传统的人机设备限制,更符合人际交流习惯。从其实现原理来看,非接触式手势控制交互识别有多种实现,有基于摄像头的识别,也有基于体感遥控的手势识别。本文采用了基于红外传感器的动态手势识别,其基本实现原理是利用四个定向二极管来感知反射的红外线能量,然后将该数据转换为四个方向的距离信息。对于如何从距离信息识别手势,一般多采用动态时间规整、人工神经网络以及隐马尔可夫模型(HMM, Hidden Markov Model)等模式匹配算法。HMM是一种随时间变化的信号模型,具有自动分割和分类能力,适合进行动态手势识别。本文通过HMM对传感器输出的四个方向距离信息进行训练识别,经过多次试验及调整,使得对于5种手势的识别率平均都达到了75%以上,并且随着手势训练数据的增加,识别率会随之提高。 相似文献
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一种稳健的基于Visemic LDA的口形动态特征及听视觉语音识别 总被引:4,自引:0,他引:4
视觉特征提取是听视觉语音识别研究的热点问题。文章引入了一种稳健的基于Visemic LDA的口形动态特征,这种特征充分考虑了发音时口形轮廓的变化及视觉Viseme划分。文章同时提出了一利利用语音识别结果进行LDA训练数据自动标注的方法。这种方法免去了繁重的人工标注工作,避免了标注错误。实验表明,将'VisemicLDA视觉特征引入到听视觉语音识别中,可以大大地提高噪声条件下语音识别系统的识别率;将这种视觉特征与多数据流HMM结合之后,在信噪比为10dB的强噪声情况下,识别率仍可以达到80%以上。 相似文献
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Lee Tranzai Zheng Fang Wu Wenhu Chen Daowen 《电子科学学刊(英文版)》2000,17(3):242-247
The co-articulation is one of the main reasons that makes the speech recognition difficult. However, the traditional Hidden Markov Models(HMM) can not model the co-articulation, because they depend on the first-order assumption. In this paper, for modeling the co-articulation, a more perfect HMM than traditional first order HMM is proposed on the basis of the authors' previous works(1997, 1998) and they give a method in that this HMM is used in continuous speech recognition by means of multilayer perceptrons(MLP), i.e. the hybrid HMM/MLP method with triple MLP structure. The experimental result shows that this new hybrid HMM/MLP method decreases error rate in comparison with authors' previous works. 相似文献
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This paper presents a novel approach for human activity recognition (HAR) using the joint angles from a 3D model of a human body. Unlike conventional approaches in which the joint angles are computed from inverse kinematic analysis of the optical marker positions captured with multiple cameras, our approach utilizes the body joint angles estimated directly from time‐series activity images acquired with a single stereo camera by co‐registering a 3D body model to the stereo information. The estimated joint‐angle features are then mapped into codewords to generate discrete symbols for a hidden Markov model (HMM) of each activity. With these symbols, each activity is trained through the HMM, and later, all the trained HMMs are used for activity recognition. The performance of our joint‐angle–based HAR has been compared to that of a conventional binary and depth silhouette‐based HAR, producing significantly better results in the recognition rate, especially for the activities that are not discernible with the conventional approaches. 相似文献
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基于HMM/VQ的认人的中等词表连续语音识别 总被引:2,自引:2,他引:0
本文讨论基于隐马尔可夫模型(HMM)和矢量量化(VQ)的连续语音识别方法。用这种方法,对每个单词作成一个HMM,对多个模型组合成的状态转移网络搜索其状态转移的最佳路径,从而实现不预先进行单词切分的连续语音的识别,使用有限态文法约束及其它一些改善识别性能的措施,演示系统能识别特定人的18种英语句式,150个单词,用312个话句(共有2710个单词)进行测试,识别延迟时间为发音时长的62%,发音速度平均为每秒2.32个单词,单词识准率为97.3%。 相似文献
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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. 相似文献
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本文简要讨论了在基于HMM的连续语音识别系统中怎样选取基本语音单元的问题,介绍了在欧洲Polyglot课题下在法国LINSI-CNRS建立的基于上下文无关音素HMM。然后,本文详细给出了利用左或右上下文相关音素HMM,作者对上述系统改进后进行的连续语音识别,有用美国语音库DARPA-RM1,在不考虑句法信息时,我们获得了连续时词识别率大约3-10个百分点的明显提高。实验是在法国LIMSI-CNRS 相似文献
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隐马尔可夫模型(HMM)已成为语音识别中的主流技术,首先介绍了语音识别技术的原理和结构,然后介绍了HMM的三个基本问题及其解决方法,最后利用Matlab仿真工具设计了一个孤立词的语音识别系统,实现了数字0~9的识别. 相似文献
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一种改进的线性区分分析方法及其在汉语数码语音识别上的应用 总被引:1,自引:0,他引:1
尽管汉语数码语音识别只涉及十个数字,但由于不同数字的发音存在相同或相似的声母或韵母,造成汉语数码语音之间的混淆性很大.采用通常的隐含马尔科夫模型(HMM)作为汉语数码语音识别模型难以得到很高的识别率.为了解决汉语数码之间的混淆问题,提高汉语数码语音识别性能,本文在隐含马尔科夫模型的状态层次上采用线性区分分析方法,将不同状态之间容易混淆的特征样本构成混淆模式类,针对混淆模式类进行线性区分分析.通过线性区分变换,在变换特征空间中仅保留那些能够有效区分该混淆类别的特征参数.这种基于状态的线性区分分析有效地提高了模型对混淆数码的区分能力.实验表明即使采用状态数很少的粗糙识别模型,也能很大幅度提高模型的识别性能;经过线性区分变换优化后的汉语数码识别模型,孤立汉语数码语音识别率可以达到99.32%. 相似文献
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A conventional environment adaptation for robust speech recognition is usually conducted using transform‐based techniques. Here, we present a discriminative adaptation strategy based on a multi‐condition‐trained model, and propose a new method to provide universal application to a new environment using the environment's specific conditions. Experimental results show that a speech recognition system adapted using the proposed method works successfully for other conditions as well as for those of the new environment. 相似文献