Abstract: Aiming at the problem of low accuracy and poor robustness of language recognition under low signal-to-noise ratio of broadcast speech signals,a language recognition algorithm based on wavelet packet transform to improve MFCC and energy operator cepstrum features is proposed.Firstly,the WMFCC feature parameters are obtained by using wavelet packet transform instead of Fourier transform and Mel filter in MFCC.On the basis of retaining the auditory perception characteristics of the human ear,the high-frequency analysis ability and analysis accuracy of the speech signal are improved,and the limitations of the Fourier transform are overcomed.Secondly,the Teager energy operator cepstrum is extracted to obtain the characteristics of the instantaneous energy of the speech,which is fused with the improved MFCC feature parameters to obtain a new feature parameter TWMFCC.Finally,in order to further improve the recognition effect of low SNR speech,a VMD adaptive Wiener filtering denoising algorithm is proposed.The experiment compares the recognition effect of the proposed features with the traditional features.The average recognition accuracy of the proposed features is significantly improved,which is 13.02 % higher than that of the traditional MFCC without speech denoising.It effectively alleviates the problem of low recognition accuracy of traditional features under low signal-to-noise ratio,and has strong anti-noise and robustness.

Key words: Language recognition, MFCC, Wavelet packet transform, Energy operator cepstrum, GMM-UBM