【作者】 刘旸；

【导师】 刘志镜； 裘国永；

【作者基本信息】 西安电子科技大学 ， 计算机技术， 2009， 硕士

【摘要】 机器人发展至今,对于机器人的控制,语音控制无非是最自然、最便捷的控制方式。从国内外对语音识别技术的研究现状来看,与机器人进行交流,把语音识别技术应用于机器人,正成为目前研究的热点。语音识别技术使机器人能听懂人的自然语言,由识别得到的信息作为声控信号应用到机器人的多种技术领域。将语音识别技术应用于机器人为使用者带来了极大的方便。因此研究并开发实用的机器人语音识别系统对于机器人的广泛应用具有重要的意义。论文的主要内容如下：首先,基于语音识别的基本原理,研究了面向机器人对话的语音识别的关键技术,语音信号的预处理,包括采样、去除噪音、端点检测、预加重、加窗分帧等；对线性预测倒谱系数(LPCC)与Mel频率倒谱系数(MFCC)的性能进行了对比分析；研究了主流的模型训练和模式匹配技术,这是语音识别技术的核心部分,包括隐马尔可夫模型(HMM)、动态时间规整(DTW)、矢量量化(VQ)、人工神经网络(ANN)、HMM和ANN的混合模型等。其次,设计完成了机器人的语音识别控制系统。基于VC++的集成开发环境编写了语音识别控制系统的软件,实现了识别性能较好、执行效率较高的机器人语音指令识别算法。并在AS-R机器人上进行了测试,结合声纳和PSD传感器的使用,大大的提高了机器人的交互性。实验结果表明,实现的语音识别控制系统的识别性能较好。同时,该系统结构简单,性价比高,易于功能扩展和移植,具有广阔的应用前景。更多还原

【Abstract】 So far the development of robot, as to the robot control, voice control is nothing but the most natural and most convenient. From the present research on voice recognition technology at home and abroad, the exchanging with robot and applying speech recognition technology to robot is becoming a hot spot of the present study.Speech recognition technology allows the robot can understand natural language. The identified information received as a voice signal is applied to a variety of robot technology. Applying the voice recognition technology to robot will bring users the greatest convenience. Therefore research and development of practical speech recognition system for robots makes great sense to the wider use of robots. The main contents of this paper are as follows:First of all, based on the basic principles of speech recognition, we research on the key technologies of robot dialogue oriented speech recognition, pre-processing of speech signal, including sampling, removing noise, endpoint detection, pre-emphasis, windowing separate frame and so on. The performance of the line Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are also compared and analyzed. We then study the mainstream model training and pattern-matching technology, which is the core of the speech recognition technology, including Hidden Markov Model (HMM), dynamic time warping (DTW), vector quantization (VQ), artificial neural network (ANN), HMM and ANN hybrid model, etc.Secondly, we design and complete the robot speech recognition control system, the speech recognition control software is compiled based on VC++integrated development environment. It can achieve better recognition performance and higher efficiency in the implementation of the robot voice command recognition algorithm. Finally it is tested on AS-R robot. Combined with sonar and the use of PSD sensors, it has greatly increased the interactivity of the robot.The experimental results show that the implementation of the speech recognition control system has better recognition performance. At the same time, the system is simple, cost-effective, easy-function expansion and transplantation. And it has a good prospect of broad application.更多还原