基于子带分析稳健的说话人识别
Robust speaker recognition based on subband analysis
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摘要: 针对说话人识别系统易受噪声干扰的问题,考虑语音的非平稳特性,以语音信号各个频段区间所含有信号能量大小和所携带信息量多少的不同为前提假设,以大量的试验为基础,研究语音信号各个子带对噪声的稳健性、所含信息的充分性。研究发现,语音信号的低频区携带大量的说话人个性信息,并且能量集中,不易被噪声干扰,再通过适当地处理,使系统在10dB信噪比下识别率超过90%。Abstract: Speaker recognition system is susceptible to noise.On this problem,the non-stationary of the speech signal needs to be considered based on the assumption that the energy and information are different in different sub-band signals.So,the stability of the noise and the completeness of the information for different sub-band signals are analyzed.Through a large number of experiments,it can be found that the low frequency speech signal,which can not only bring a large number of personalized information but also concentrate much energy,is not easy being interfered by noise.Some appropriate treatment has been adopted to make the system's recognition rate over 90% for the signal to noise ratio of 10dB.