计算机理论论文汉语孤立字语音识别技术的研究.doc

汉语孤立字语音识别技术的研究 汉语孤立字语音识别技术的研究 study on isolated mandarin speech recognition technology 【摘要】 人类有个理想,让机器具有“听”、“说”人类语言的能力。这个理想,在信息时代正逐步变成现实。语音识别正是解决机器“听”懂人类语言的一项研究。孤立词语音识别实现简单、技术成熟,有着广泛的应用前景,是深入研究语音识别的基础。本文对小词汇量、非特定人的汉语孤立词的语音识别技术进行了分析和研究。首先介绍了语音识别系统的组成和识别原理,并对语音信号的预处理过程、端点检测常用的特征参数以及语音识别的方法作了分析,重点讨论了mfcc特征参数的提取。继而重点研究了孤立词的端点检测算法,并在基于信息熵、子带谱熵和频带方差的端点检测算法的基础上,对原有算法做了修正和改进,仿真结果表明,在不同噪声下,基于改进的端点检测算法在低信噪比条件下的检测准确率明显高于传统的基于能量和过零率的双门限检测算法,其中基于改进的频带方差的检测效果最好。最后深入研究了基于dtw和hmm的语音识别方法。其中基于dtw的高效算法具有运算复杂度低的特点,仿真结果表明,它非常适合于小词汇量、特定人的语音识别,识别率可以达到100%。但是对于非特定人识别,本文选用主流的基于hmm的识别方法,并仔细探讨了chmm应用到语音识别中的具体问题。结合改进的端点检测算法并采用基于chmm的语音识别方法,最终实现了对自建的汉语孤立数字语音库92%的平均识别率。【abstract】 man has long dreamed of having a machine that can “listen to” and “speak” human languages. this ideal of man, in the information era, is gradually becoming a reality with the state-of-the-art technology in speech recognition, the task of which is to solve the problem of machine understanding the human speech.isolated-word speech recognition is the foundation of further deep research on speech recognition, which is easy to implement, with its technique mature and its application prospect broad. in this paper, the technique of small-vocabulary speaker-independent isolated-word speech recognition is analyzed and researched.firstly, this paper focuses on the introduction of the fundamentals of speech recognition. the components and principles of a typical speech recognition system is presented in simple, then the speech signal preprocess, the endpoint detection feature parameters and the speech recognition methods are analyzed, further the extraction of mel frequency cepstrum coefficients (mfcc) feature is discussed in detail.secondly, the isolated-word endpoint detection algorithms are mainly researched. based on the endpoint detection algorithms of information entropy, band-partitioning spectral entropy and variance of frequency, revisions and ameliorations are made on the original algorithms and corresponding improved endpoint detection algorithms are proposed, the simulation results under the same snr conditions show that the detection accuracy rate of the improved endpoint detection algorithms is significantly higher than that of the traditional threshold detection algorithm based on energy and zero-crossing, wherein the detection performance of the improved variance of frequency based algorithm is the best.finally, speech recognition methods based on dynamic time warping (dtw) and hidden markov model (hmm) are deeply studied. the fast dtw algorithm has low complexity and is very suitable for small-vocabulary speaker-dependent speech recognition. the experimental data shows that its correct identification rate is almost up to 100%. for speaker-independent speech recognition, hmm-based mainstream identification methods is used in this paper, the specific issues of continuous hmm applied to speech recognition are also discussed. ultimately, combining the improved endpoint detection algorithms with continuous hmm recognition method, an average recognition rate of up to 92% is achieved in the recognition of self-built chinese figures voice database. 【关键词】 孤立词识别; 非特定人; 端点检测; 美尔频率倒谱参数; 动态时间规整; 隐马尔可夫模型 【key words】 isolated-word recognition; speaker-independent; endpoint detection; mel frequency cepstrum coefficients; dynamic time warping; hidden markov model 汉语孤立字语音识别技术的研究摘要 6-7 abstract 7-8 第1章 绪论 11-16 1.1 语音识别的基本概念 11-12 1.2 语音识别技术的发展历程 12-13 1.3 语音识别研究现状和面临的挑战 13-15 1.4 论文的研究内容和结构安排 15-16 第2章 语音识别的基本原理 16-33 2.1 语音信号的产生及数学模型 16-18 2.2 语音识别系统的组成及其识别原理 18-19 2.3 语音信号的预处理 19-22 2.3.1 语音信号数字化 19 2.3.2 预加重处理 19-20 2.3.3 加窗和分帧处理 20-22 2.4 端点检测常用的特征参数 22-26 2.4.1 时域特征参数 23-24 2.4.2 频域特征参数 24-26 2.5 特征提取 26-32 2.5.1 线性预测倒谱系数(lpcc) 27 2.5.2 美尔频率倒谱参数(mfcc) 27-32 2.6 语音识别方法简介 32-33 第3章 基于孤立词的端点检测算法研究 33-48 3.1 噪声源和信噪比 33-35 3.2 语音端点检测算法及其改进 35-48 3.2.1 基于短时能量和短时过零率的语音端点检测方法 35-36 3.2.2 基于信息熵的语音端点检测方法及其改进算法 36-41 3.2.3 基于改进的子带谱熵的端点检测算法 41-44 3.2.4 基于能量加权的频带方差的端点检测算法 44-48 第4章 语音识别算法研究 48-69 4.1 动态时间规整(dtw)算法 48-56 4.1.1 dtw算法的匹配原理 48-52 4.1.2 一种改进的高效dtw算法 52-55 4.1.3 dtw模板训练和识别 55-56 4.2 隐马尔可夫模型(hmm)在语音识别中的运用 56-69 4.2.1 隐马尔可夫模型 57-58 4.2.2 hmm中的三个基本问题及其解决方案 58-63 4.2.3 hmm在语音识别应用中的具体问题 63