Abstract
High-pass or band-pass filtering of log subband energies has been shown to improve the robustness of automatic speech recognition to convolutional channel distortions. The authors compare several such filters and apply them in the PLP cepstral domain as well as the log subband domain. They evaluate the robustness of these techniques to Lombard-style test speech with additive noise and their ability to cancel channel effects. They explicitly examine the interactions of such high-pass or band-pass filters with cepstral time derivatives (which are themselves high-pass functions). Conclusions are drawn about factors (e.g., log subband vs cepstral domain, high-pass vs band-pass filter characteristics, and use of time derivatives) which determine the success of these filtering approaches for speaker-independent speech recognition in distorted-channel and noisy-Lombard conditions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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