ADAPTING HMMS OF DISTANT-TALKING ASR SYSTEMS USING FEATURE-DOMAIN REVERBERATION MODELS
BibTeX
@MISC{Sehr_adaptinghmms,
author = {Armin Sehr and Markus Gardill and Walter Kellermann},
title = {ADAPTING HMMS OF DISTANT-TALKING ASR SYSTEMS USING FEATURE-DOMAIN REVERBERATION MODELS},
year = {}
}
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Abstract
To capture the dispersive effect of reverberation by Hidden Markov Model (HMM)-based distant-talking speech recognition systems, adapting the means of the current HMM state based on the means of the preceding states has been suggested in [1]. In this contribution, we propose to incorporate the reverberation models of [2] into the adaptation approach to describe the effect of reverberation with higher accuracy. Connected-digit recognition experiments in three different rooms confirm that the suggested more accurate reverberation representation leads to a significant performance increase in all investigated environments. 1.
