Abstract：In current language identification system, the commonly used feature parameters have not made the best use of auditory characteristics and have weak robustness in complex environments. An auditory-based robust feature extraction algorithm is proposed. Each sub-band energy of the extracted auditory features is calculated by using a Gammachirp filter bank instead of the commonly used triangle filter bank. The compensation filter using data-driven analysis for each sub-band output is obtained by a constrained optimization process which jointly minimizes the environmental distortion as well as the distortion caused by the filter itself. Experimental results show that the feature outperforms the Mel-frequency cepstral coefficient widely used in noisy environments.
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2012, Vol. 25 
