Abstract: Aiming at the problem of low accuracy of language identification under low signal to noise ratio, a fusion identification method is proposed, using spectral parameters of channel impulse response and Teager energy operators cepstral coefficients. Considering the distribution of different feature information in speech, a low-pass filter is introduced to filter out the high-frequency part of the signal in the front-end of feature extraction. The resampling method is used to reduce the rate. And then, the spectral parameters of channel impulse response of vocal tract are extracted, and fused with the Teager energy operators cepstral coefficients. Finally, a Gaussian mixture model-universal background model is used to perform the language identification. Experiments under different signal to noise ratio conditions show that the proposed methold significantly improves the language identification accuracy with 15 dB gain at low signal to noise ratio compared with the single Mel frequency cepstrum coefficient feature, single Gammatone frequency cepstrum coefficient feature and log Mel-scale filter bank energies feature.

Key words: language identification, spectral parameters of channel impulse response, low-pass filtering, resampling, Teager energy operators cepstral coefficients