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Auditory-Based Feature Extraction and Robust Speaker Identification

  • Qi (Peter) LiEmail author
Chapter
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Part of the Signals and Communication Technology book series (SCT)

Abstract

In the previous chapter, we introduced a robust auditory transform (AT). In this chapter, we present an auditory-based feature extraction algorithm based on the AT and apply it to robust speaker identification. Usually, the performances of acoustic models trained in clean speech drop significantly when tested in noisy speech. The presented features, however, have shown strong robustness in this kind of situation. We present a typical text-independent speaker identification system in the experiment section. Under all three different mismatched testing conditions, with white noise, car noise, or babble noise, the auditory features consistently perform better than the baseline mel frequency cepstral coefficient (FMCC) features. The auditory features are also compared with perceptual linear predictive (PLP) and RASTA-PLP features, The features consistently perform much better than PLP. Under white noise, the FMCC features are much better than RASTA-PLP. Under car and babble noises, the performace are similar.

Keywords

Fast Fourier Transform Hair Cell Discrete Cosine Transform Basilar Membrane Speaker Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  1. 1.Li Creative Technologies (LcT), IncFlorham ParkUSA

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