该文研究了基于数据模拟方法和HMM（隐马尔科夫模型）自适应的电话信道条件下语音识别问题。
This paper addresses the problem of speech recognition under telephone channel conditions using data simulation method and HMM(Hidden Markov Model)adaptation.
本文研究神经网络的多层感知器模型在语音识别中的应用。
This paper describes the use of multi-layer perception model of neural network in speech recognition.
最后，在中文语音识别任务上测试两类语言模型的性能。
Finally, these two language models are evaluated on the task of Chinese speech recognition.
提出了一种隐马尔可夫模型（HMM）和径向基函数神经网络（RBF）相结合的语音识别新方法。
Presents a new hybrid framework of hidden Markov models (HMM) and radial basis function (RBF) neural networks for speech recognition.
为了解决这些问题，我们提出了基于数字语音时频信息整体结构的单特征向量识别模型。
In order to solve these problems, we proposed a single feature vector recognition model based on whole time-frequency information structure of digit speech.
提出了一个柔性可扩展体系结构非特定人语音识别系统的框架模型，介绍了相关实现原理。
A framework model of independent speech recognition system based on the flexible and extensible architecture is put forward, and some correlative theories are introduced.
实验基于AURORA语音数据库，并用其所带的汽车噪声环境下的测试集对模型进行了识别验证。
We recognize the models using the test set in noisy car environment which is included in AURORA speech database.
该系统利用大词汇量非特定人连续语音识别技术与口语对话模型实现了智能熊猫系统的人机知识问答。
Human-ma-chine interlocution of Smartpanda System is implemented by means of speaker independent continuous speech recognition technology and dialogue model.
研究适用于隐马尔可夫模型(HMM)结合多层感知器(mlp)的小词汇量混合语音识别系统的一种简化神经网络结构。
It is applicable to any small vocabulary hybrid speech recognition system that combines hidden Markov model (HMM) with multi-layer perceptron (MLP).
本文介绍多码本离散隐马尔可夫模型用于含噪声语音识别的研究成果。
A discrete hidden Markov model based on the multiple vector quantization codebooks is used here for speaker-dependent discrete speech recognition in Noisy Environments.
但直接将该模型用于语音识别，将会使网络产生规则灾和网络推理失效等问题。
But if this model is applied in speech recognition directly, it would produce the problems of rule disaster and network ratiocination invalidation.
完整的连续语音识别系统主要包括四个部分：特征提取，声学模型，语言模型和搜索算法，本文就是根据这四个部分展开的。
A whole continuous speech recognition system includes four parts: feature extraction, acoustic model, language models and search algorithms, and the thesis is carried out according to them.
文中在一个大型数据库上对语音识别基元、语音模型、模型的输出观察向量的计分方法进行了大量的比较实验。
A great deal of experiments on speech recognition units, speech recognition models and the forms of scoring methods for output observation vectors have been done based on a giant speech corpus.
测试实验证明了SDSPM模型在汉语语音识别中的有效性。
The SDSPM feasibility is tested in an actual Chinese speech recognition environment.
仅仅依靠语音信号的声学模型来进行语音识别，存在着不能利用语言的非声学知识的固有缺陷。
Traditional speech recognition system has an intrinsic defect that, commonly only use the acoustic model of speech and unable to use non-acoustic knowledge of language to recognize speech.
新模型使语音识别率得到了改善。
本文根据耳语音信号发音模型，结合耳语音的声学特性，建立了一个汉语耳语音孤立字识别系统。
In this paper, a Chinese isolated word recognition system is established based on the source-filter generation model combined with the acoustic characteristics of whispered speech.
由于在语音识别中被广泛应用的隐马尔可夫模型(HMM)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。
Since the widely used Hidden Markov model (HMM) in speech recognition is first order Markov model, it can not fully model the temporal dependence of speech signal.
而语言模型、语法及词法模型在中、大词汇量连续语音识别中是非常重要的。
Furthermore it is very important that we use language model, syntax and accidence model in middle or big glossary continuous speech recognition.
理论上详细介绍了HMM模型及与语音识别相关的语音数字信号处理。
This paper also introduces HMM model and speech digital signal processing associated with speech recognition .
将基于连续隐含Markov模型 语音 识别算法中占系统总运算量的50%以上的Mahalanobis距离 计算，映射为硬件实现的 模块。
The mapped algorithm, called the Mahalanobis distance, handles about 50% of its computational load in the overall speech recognition algorithm using a continuous hidden Markov model(CHMM).
第二部分讲述语音识别系统中的有关演算法，包括模式分类，查找演算法，随机模型，语言模型等技术。
Part II describes algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques.
非齐次hmm模型是提高语音识别性能的潜在的重要方法。
Using the inhomogeneous HMM is a potential way to improve the performance of speech recognition system.
本文从语音模型入手，讨论了新的抗噪语音识别方法。
This paper starts from the speech models, and main works focus on developing new noisy speech recognition methods.
针对上述问题，结合隐马尔可夫模型原理，在HTK语音处理工具箱的基础上构建了中英文特定词语音识别系统。
To address the problem above, the speech recognition system has been built on the basis of HTK as well as hidden markov model theory.
结果表明：所设计的语音识别算法有很高的识别率，能减小或者消除噪声所带来的训练模型和测试语音之间的失配。
The results show that the speech recognition algorithm has high recognition rate, can reduce or eliminate noise caused by the training model and the mismatch between the speech test.
本文首先介绍了说话人识别系统的概念，然后分析了几种常用的语音特征参数的提取方法以及说话人识别的几种模型。
This text introduced the concept of speaker recognition system firstly, then analyzed a few extraction methods of speech feature parameters in common use and a few models of speaker recognition.
然后研究了用于语音识别的两种方法：隐马尔可夫模型(HMM)和人工神经网络(ANN)。
And then, two methods of speech recognition are researched: Hidden Markov Mode1 (HMM) and Artificial Neutral Net (ANN).
其次，在非特定人语音识别技术方面，文章研究了现行最流行基于隐马尔可夫模型的非特定人语音识别技术。
Secondly, the hidden Markov model which is the most popular speech recognition technology has been studied in the way of speaker indendent.
语音的声学模型和识别理论是构建语音识别系统的基础。
Acoustic model and speech recognition theory is the basis for building speech recognition systems.
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