非特定人的语音识别系统研究

发布时间：2018-03-26 21:39

本文选题：语音识别　切入点：非特定人　出处：《安徽工业大学》2016年硕士论文

【摘要】：在科学技术发展的推动下,语音识别技术已经逐渐从研究阶段进入到实际应用阶段。但是,对非特定人的语音识别研究仍在激烈的探讨中,怎样提高该系统的识别率,依旧是当前研究的热点问题。本文系统地研究了语音识别系统的各个组成部分,针对部分关键技术提出了改进的算法,并在MATLAB上建立了相应的非特定人识别系统。文中深入研究了语音识别系统的原理组成部分,包括语音信号的预处理、起止端点的检测、特征参数的提取。在此基础上,对三种常用的语音识别方法:动态时间规整(DTW)、隐马尔科夫模型(HMM)与神经网络模型(ANN)进行了对比分析,并重点研究了隐马尔科夫模型算法,对该算法中存在的数据溢出问题采取了有效的解决措施。接着,针对低信噪比噪声环境下,语音信号的滤波和端点检测这两个关键技术,分别提出了改进的算法,即:基于经验模式分解(EMD)和奇异值分解(SVD)差熵法的滤波算法,以及改进的希尔伯特黄变换(HHT)语音端点检测算法,并将改进后的算法分别与传统算法的处理结果进行了分析比较。本文在MATLAB平台上建立了基于HMM模型的非特定人的语音识别系统。结果表明,与传统的滤波方法以及端点检测方法相比,改进后的算法提高了识别系统的识别率,充分体现了改进算法的有效性和可行性。最后设计了一个语音识别系统GUI界面,包括语音信号处理的界面和语音的识别过程界面,对语音库中的语音进行实时识别实验,验证了所用系列方法的有效性。
[Abstract]:With the development of science and technology, speech recognition technology has gradually moved from the research stage to the practical application stage. However, the research on the speech recognition of non-specific people is still under intense discussion, how to improve the recognition rate of the system, It is still a hot topic in current research. In this paper, the components of speech recognition system are systematically studied, and an improved algorithm is proposed for some key technologies. In this paper, the principle of speech recognition system is deeply studied, including speech signal preprocessing, endpoint detection and feature parameter extraction. Three common speech recognition methods: dynamic time warping (DTW), Hidden Markov Model (HMMM) and Neural Network (Ann) are compared and analyzed. This paper takes effective measures to solve the problem of data overflow in the algorithm. Then, aiming at the two key technologies of speech signal filtering and endpoint detection in low signal-to-noise noise environment, the improved algorithm is proposed respectively. That is, the filtering algorithm based on empirical mode decomposition (EMD) and singular value decomposition (SVD) differential entropy method, and the improved Hilbert Huang transform (HHT) speech endpoint detection algorithm. The improved algorithm is analyzed and compared with the results of the traditional algorithm. In this paper, a speech recognition system based on the HMM model is established on the MATLAB platform. The results show that, Compared with the traditional filtering method and the endpoint detection method, the improved algorithm improves the recognition rate of the recognition system, and fully reflects the effectiveness and feasibility of the improved algorithm. Finally, a speech recognition system GUI interface is designed. It includes the interface of speech signal processing and the interface of speech recognition process. The experiments of speech recognition in speech database are carried out in real time, and the validity of the series of methods is verified.
【学位授予单位】：安徽工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN912.34

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