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Feature Compensation Employing Variational Model Composition for Robust Speech Recognition in In-Vehicle Environment

  • Wooil Kim
  • John H. L. Hansen
Chapter

Abstract

This chapter proposes a novel model composition method to improve speech recognition performance in time-varying background noise conditions. It is suggested that each order of the cepstral coefficients represents the frequency degree of changing components in the envelope of the log-spectrum. With this motivation, in the proposed method, variational noise models are generated by selectively applying perturbation factors to a basis model, resulting in a collection of various types of spectral patterns in the log-spectral domain. The basis noise model is obtained from the silent duration segments of input speech. The proposed Variational Model Composition (VMC) method is employed to generate multiple environmental models for our previously proposed feature compensation method. Experimental results prove that the proposed method is considerably more effective at increasing speech recognition performance in time-varying background noise conditions with +20.80% relative improvement in word error rates for the CU-Move real-life in-vehicle corpus, compared to an existing single model–based method.

Keywords

Feature compensation In-vehicle environment Multiple model Robust speech recognition Variational model composition (VMC) 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Center for Robust Speech Systems (CRSS), Erik Jonsson School of Engineering and Computer ScienceUniversity of Texas at DallasRichardsonUSA

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