Speaker identification based on the use of robust cepstral features obtained from pole-zero transfer functions

A common problem in speaker identification systems is that a mismatch in the training and testing conditions sacrifices much performance. We attempt to alleviate this problem by proposing new features that show less variation when speech is corrupted by convolutional noise (channel) and/or additive noise. The conventional feature used is the linear predictive (LP) cepstrum that is derived from an all-pole transfer function which, in turn, achieves a good approximation to the spectral envelope of the speech. A different cepstral feature based on a pole-zero function (called the adaptive component weighted or ACW cepstrum) was previously introduced. We propose four additional new cepstral features based on pole-zero transfer functions. One is an alternative way of doing adaptive component weighting and is called the ACW2 cepstrum. Two others (known as the PFL1 cepstrum and the PFL2 cepstrum) are based on a pole-zero postfilter used in speech enhancement. Finally, an autoregressive moving-average (ARMA) analysis of speech results in a pole-zero transfer function describing the spectral envelope. The cepstrum of this transfer function is the feature. Experiments involving a closed set, text-independent and vector quantizer based speaker identification system are done to compare the various features. The TIMIT and King databases are used. The ACW and PFL1 features are the preferred features, since they do as well or better than the LP cepstrum for all the test conditions. The corresponding spectra show a clear emphasis of the formants and no spectral tilt.