Abstract
The characterization of a speech signal using non-linear dynamical features has been the focus of intense research lately. In this work, the results obtained with time-dependent largest Lyapunov exponents (TDLEs) in a text-dependent speaker verification task are reported. The baseline system used Gaussian mixture models (GMMs), obtained from the adaptation of a universal background model (UBM), for the speaker voice models. Sixteen cepstral and 16 delta cepstral features were used in the experiments, and it is shown how the addition of TDLEs can improve the system’s accuracy. Cepstral mean subtraction was applied to all features in the tests for channel equalization, and silence frames were discarded. The corpus used, obtained from a subset of the Center for Spoken Language Understanding (CSLU) Speaker Recognition corpus, consisted of telephone speech from 91 different speakers.
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