Application of Improved Hybrid Model Based on HMM and BP Neural Nework in Speech Recognition

语音识别是一门内容丰富、应用广泛的技术。本文着眼于汉语语音识别的主要问题，研究汉语语音孤立词识别的关键技术，以提高语音的识别率和识别模型的收敛速度。本文论述了语音识别的基本原理，从语音信号的时域、频域、倒谱域出发，对语音信号进行分析，介绍了语音信号分析方法中的滤波器组分析方法和线性预测编码技术，并推导了线形预测倒谱系数(LPCC)和Mel倒谱系数(MFCC)。在特征提取中，选用了基于听觉模型的MFCC，并与基于发声模型的LPCC参数进行分析比较。隐马尔可夫模型(HMM)和人工神经网络在语音信号处理中都有广泛的应用，本文剖析了两者在语音信号处理上各自的优缺点。为取HMM和人工神经网络这两种模型各...Speech recognition is a technology which has rich content and has been widely used. This thesis focuses on the main issues of Chinese speech recognition. In order to improve the recognition ratio and speed up convergence, the key technologies of the Chinese speech recognition has been researched. This thesis analyzes the speech signal and describes the principle of speech recognition from the t...学位：工学硕士院系专业：软件学院_计算机应用技术学号：20034001

基于HMM 与神经网络的声学模型研究

神经网络能依靠权值进行长时间记忆和知识存储,但是对输入模式的瞬时相应的记忆能力比较差;而隐马尔科夫模型的短时记忆的能力比较强,但是假定的前提又与实际情况不符. 因此,采用HMM 和ANN 的混合模型来取双方之长,并在这种混合模型的基础上,对神经网络从结构设计、训练、到训练后期的结构调整进行了全程的优化;应用隐节点剪枝算法,并利用广义的Hebb 规则重新确定网络的参数. 实验表明,这种混合模型在语音识别中取得了良好的效果.厦门大学985 二期信息创新平台项

Key Technology Research for Speech Recognition

采用隐马尔可夫模型(HMM)进行语音声学建模是大词汇连续语音识别取得突破性进展最主要的原因之一,HMM本身依赖的某些不合理建模假设和不具有区分性的训练算法正在成为制约语音识别系统未来发展的瓶颈。神经网络依靠权能够进行长时间记忆和知识存储,但对于输入模式的瞬时响应的记忆能力比较差。采用混合HMM/ANN模型对HMM的一些不尽合理的建模假设和训练算法进行了革新。混合模型用神经网络非参数概率模型代替高斯混合器(GM)计算HMM的状态所需要的观测概率。另外对神经网络的结构进行了优化,取得了很好的效果。Because of the application of the Hidden Markov Model(HMM) in acoustic modeling,a significant breakthrough has been made in recognizing continuous speech with a large glossary.However,some unreasonable hypotheses for acoustic modeling and the unclassified training algorithm on which the HMM based form a bottleneck,restricting the further improvement in speech recognition.The Artificial Neural Network(ANN) techniques can be adopted as an alternative modeling paradigm.By means of the weight values of the network connections,neural networks can steadily store the knowledge acquired from the training process.But they possess a weak memory,not being suitable to store the instantaneous response to various input modes.To overcome the flaws of the HMM paradigm,we design a hybrid HMM/ANN model.In this hybrid model,the nonparametric probabilistic model(a BP neural network) is used to substitute the Gauss blender to calculate the observed probability which is necessary for computing the states of the HMM model.Besides,we optimize the structure of the network,and experiments show that the hybrid model has a good performance in speech recognition.厦门大学985二期信息创新平台项目资

An Acoustic Model Based on HMM/ANN

神经网络能依靠权值进行长时间记忆和知识存储,但是对输入模式的瞬时相应的记忆能力比较差;而隐马尔科夫模型的短时记忆的能力比较强,但是假定的前提又与实际情况不符.因此,采用HMM和ANN的混合模型来取双方之长,并在这种混合模型的基础上,对神经网络从结构设计、训练、到训练后期的结构调整进行了全程的优化;应用隐节点剪枝算法,并利用广义的Hebb规则重新确定网络的参数.实验表明,这种混合模型在语音识别中取得了良好的效果.The Artificial Neural Network(ANN) can depend on weight values to store memory and knowledge for a long time.However it possesses a weak memory,not being suitable to store the instantaneous response to various input modes.The Hidden Markov Model(HMM) is better in instantaneous memory,but the presupposition precondition is not according with the real situation.So we design a hybrid HMM/ANN model to overcome the flaws of using either of them.And basing on this model,we make a global optimization for ANN in structure design,training and structure adjustment in the later period of training.We propose an algorithm to prune hidden nodes in a trained neural network,and utilize the generalized Hebbian algorithm to reconfigure the parameters of the network.Some experiments show that the hybrid model has a good performance in speech recognition.厦门大学985二期信息创新平台项目资