【作者】 肖宜；

【导师】 黄涛；

【作者基本信息】 武汉理工大学 ， 通信与信息系统， 2008， 硕士

【摘要】 语音识别的研究工作始于上个世纪50年代,至今已经形成了完整的理论体系,目前语音识别的研究也已经进入了商品化阶段,基础性理论相当完善,各种各样的产品也相继涌现。然而语音识别在实现过程中通常涉及多种因素,需要同时考虑,并且它作为一门交叉学科,涉及到了信号处理、模式识别、人工智能、计算机科学、语言学和认知科学等众多学科,所以语音识别距离理想目标仍有很大距离,相关的技术难关还有待克服。本文对语音识别的主要过程进行了详细的介绍。语音识别首先对输入的语音信号必须进行预处理,以保证系统获得一个比较理想的处理对象。在语音的特征参数提取阶段,文中介绍了在实际应用中常用到的特征参数:线性预测倒谱参数(LPCC)、Mel频率倒谱参数(MFCC)等。在识别阶段,介绍了基于矢量量化的识别技术以及动态时间归整的识别技术(DTW)。在此基础上,引入了蚁群算法的基本原理。蚁群算法是最新发展的一种模拟昆虫王国中蚂蚁群体智能行为的仿生优化算法,它具有较强的鲁棒性、优良的分布式计算机制、易于与其他方法相结合等优点。蚁群算法作为一种新的用于解决复杂优化问题的全局搜索方法,已经成功应用于求解TSP问题、调度问题、指派问题等,显示出了蚁群算法在处理复杂优化问题方面的优越性。本文利用蚁群算法优化机制,结合传统的DTW算法,提出了一种新的基于蚁群算法的动态时间规划算法来搜索语音信号特征参数序列之间匹配的全局最优路径,进而以此衡量语音信号之间的相似度,从而使系统的识别效果有了进一步的提高。文中最后对新的语音识别系统各模块进行了仿真测试,给出了仿真计算结果。实验结果表明,采用基于蚁群算法的语音识别系统识别效果要好于采用传统DTW算法的语音识别系统。更多还原

【Abstract】 The speech recognition which has been researched since the 1950s, has developed to an integrated theory and been commoditized with perfect basic theory and lots of products successively emerging. However, the practice of speech recognition is related to various factors, which must be considered simultaneously in the process. As a cross-discipline, it also has everything to do with many subjects, such as signal processing, pattern recognition, artificial intelligent, computer science, linguistics and epistemic science. Therefore, there are still many associated technological difficulties to be conquered and the current speech recognition is still far from the final target.The main process of speech recognition is analyzed and investigated thoroughly. First, the input of speech signals must be pre-processed in the system in advance, so that the object for the system to process is comparatively ideal. Secondly, the frequently-used characteristic parameters, such as LPCC and MFCC, are introduced in detail when coming to abstracting characteristics while some key techniques including VQ and DTW are analyzed in the recognition step. Then, the basic principles of ant colony algorithm are introduced.Ant colony algorithm which is one of the algorithms latest developed, is a bionic optimization algorithm by simulating the intelligence of ants swarm in insect kingdom. As a new algorithm used to solve complex optimization problems of global search method, the ant colony algorithm with its robustness, good distributed computing mechanism and easy-combination with other methods has been successfully applied into TSP, scheduling and assignment problems, showing many advantages in dealing with the complex optimization problems.By combining ant colony algorithm optimization mechanism with the traditional DTW algorithm, a new dynamic time programming algorithm based on the ant colony algorithm is proposed, which is used to search the speech signals characteristic parameters sequences for the global optimal path, by which the similarity between the speech signals is measured. Thus, the recognition result of the system has been further improved.The new speech recognition system is tested by simulating every single module and evaluated with the result figures shown in the final part. The experimental results illustrate that the speech recognition system based on ant colony algorithm has better performance than that based on traditional DTW algorithm.更多还原