Language identification based on auditory features
Jia Liu · Journal of Tsinghua University(Science and Technology) · 2009
An auditory-based feature extraction algorithm was developed to improve the recognition performance of language identification algorithms using human auditory characteristics.The sub-band energies of the extracted auditory features were calculated using a Gammatone filter bank instead of the commonly used triangle filter bank.The center frequencies and bandwidths were then determined according to the equivalent rectangular bandwidth(ERB) model.The subjective human loudness perception for different frequency components was simulated by an inverse equal loudness curve.The first- and second-order delta cepstrum and the shifted delta cepstrum were derived based on these auditory features.Tests show that the features outperform the widely used Mel-frequency cepstrum coefficient(MFCC) counterparts.