Publication
ASRU 2011
Conference paper

Matched-condition robust dynamic noise adaptation

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Abstract

In this paper we describe how the model-based noise robustness algorithm for previously unseen noise conditions, Dynamic Noise Adaptation (DNA), can be made robust to matched data, without the need to do any system re-training. The approach is to do online model selection and averaging between two DNA models of noise: one that is tracking the evolving state of the background noise, and one clamped to the null mis-match hypothesis. The approach, which we call DNA with (matched) condition detection (DNA-CD), improves the performance of a commerical-grade speech recognizer that utilizes feature-space Maximum Mutual Information (fMMI), boosted MMI (bMMI), and feature-space Maximum Likelihood Linear Regression (fMLLR) compensation by 15% relative at signal-to-noise ratios (SNRs) below 10 dB, and over 8% relative overall. © 2011 IEEE.

Date

Publication

ASRU 2011

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