Uncertainty decoding on Frequency Filtered Parameters for Robust ASR Articles uri icon

publication date

  • May 2010

start page

  • 440

end page

  • 449

issue

  • 5

volume

  • 52

International Standard Serial Number (ISSN)

  • 0167-6393

Electronic International Standard Serial Number (EISSN)

  • 1872-7182

abstract

  • The use of feature enhancement techniques to obtain estimates of the clean parameters is a common approach for robust automatic speech recognition (ASR). However, the decoding algorithm typically ignores
    how accurate these estimates are. Uncertainty decoding methods
    incorporate this type of information. In this paper, we develop a
    formulation of the uncertainty decoding paradigm for Frequency Filtered
    (FF) parameters using spectral subtraction as a feature enhancement
    method. Additionally, we show that the uncertainty decoding method for
    FF parameters admits a simple interpretation as a spectral weighting
    method that assigns more importance to the most reliable spectral
    components.Furthermore, we suggest combining this method
    with SSBD-HMM (Spectral Subtraction and Bounded Distance HMM), one
    recently proposed technique that is able to compensate for the effects
    of features that are highly contaminated (outliers). This combination
    pursues two objectives: to improve the results achieved by uncertainty
    decoding methods and to determine which part of the improvements is due
    to compensating for the effects of outliers and which part is due to
    compensating for other less deteriorated features.