改进的高效动态时间规整算法语音识别系统

动态时间规整算法是结合了动态时间规整(DTW)技术和距离测度计算技术的一种非线性规整算法，在语音识别模板匹配中有重要的应用。为此提出一种改进的高效动态时间规整算法，其能有效加快搜索路径的寻找。基于Matlab实现了隐马尔科夫算法、高效动态时间规整算法和改进的高效动态时间规整算法的语音识别系统，同时进行了算法的仿真实验。实验结果表明，基于改进高效动态时间规整算法的训练速度远大于基于隐马尔可夫算法和高效动态时间规整算法的训练速度，而识别率下降很小，对于小词汇量非连续语音识别中高效动态时间规整算法的识别率为97.56%，隐马尔可夫算法的识别率为97.14%，改进高效动态时间规整算法的识别率为96.43%。

Improved high efficiency Dynamic Time Warping for speech recognition

The Dynamic Time Warping(DTW) algorithm is a nonlinear warping algorithm combined with dynamic time warping technique and distance measurement computing method. It has an important application in speech recognition based on template matching. This paper proposes an improved efficient dynamic time warping algorithm, which can effectively speed up the path searching in speech recognition. Based on Matlab, the hidden Markov algorithm, efficient dynamic time warping algorithm and improved dynamic time warping algorithm speech recognition systems are implemented. Simulation experiments based on above algorithms are performed. The experimental results show that the training speed of improved efficient dynamic time warping algorithm is much faster than that of efficient dynamic time warping algorithm and hidden Markov algorithm, but the recognition rate of decline based on improved efficient dynamic time warping algorithm is very low. For small vocabulary continuous speech recognition, the recognition rate is 97.56% for efficient dynamic time warping algorithm, 97.14% for hidden Markov recognition algorithm, and 96.43% for improved efficient dynamic time warping algorithm respectively.