摘要
针对语音识别过程中环境噪声干扰大的问题,提出一种基于经验模态分解(EMD)与动态时间规整(DTW)相结合的孤立词识别算法。该方法利用EMD算法,首先将提取的性能不好的语音信号分解成若干个基本模函数(IMF),去掉原始信号中的干扰和噪声。然后,基于DTW算法,采用短时过零率和短时能量对语音信号进行端点检测,提取语音特征参数后与参考模板进行匹配。将参考模板与待测模板之间的最短路径作为识别结果。仿真结果表明,该算法能够提高语音的识别效率和识别的正确率。
In order to solve the problem of large interference of environmental noise during speech recognition,an isolated word recognition algorithm based on empirical mode decomposition（EMD）and dynamic time warping（DTW）is proposed.In this method,the EMD algorithm is used to decompose the speech signal with poor performance into several basic mode functions（IMF）and remove the interference and noise in the original signal.Then,based on the DTW algorithm,the shorttime zero crossing rate and short-time energy are used to detect the endpoint detection of speech signal.After the speech feature parameters are extracted,the speech signal is matched with the reference template.Finally,the shortest path between the reference template and the template to be measured is used as the recognition result.The simulation results show that the proposed algorithm can improve the recognition efficiency of speech and the accuracy of recognition.
出处
《辽宁石油化工大学学报》
CAS
2018年第1期74-78,共5页
Journal of Liaoning Petrochemical University
基金
国家自然科学基金项目(61673199)
辽宁省高校优秀人才支持计划项目(LJQ2015061)
关键词
语音识别
经验模态分解
动态时间规整
孤立词识别
Speech recognition
Empirical mode decomposition
Dynamic time warping
Isolated word recognition
