Wavelet Packet Transform Features with Application to Speaker Identification
نویسندگان
چکیده
This study proposes a new set of feature parameters based on wavelet packet transform analysis of the speech signal. The new speech features are named subband based cepstral parameters (SBC) and wavelet packet parameters (WPP). The ability of each parameter set to capture speaker identity conveyed in the speech signal is compared to the widely used Mel-frequency cepstral coee-cents (MFCC). The proposed parametrization methods are shown to achieve 48% and 67% reduction in relative error over MFCC for 630 and 168 speakers, respectively using the TIMIT (downsampled to 8 kHz) database.
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