Abstract

We report our recent work on noise-robust large-vocabulary
speech recognition. Three key innovations are developed and
evaluated in this work: 1) a new model learning paradigm that
comprises a noise-insertion process followed by noise
reduction; 2) a noise adaptive training algorithm that integrates
noise reduction into probabilistic multi-style system training;
and 3) a new algorithm (SPLICE) for noise reduction that
makes no assumptions about noise stationarity. Evaluation on a
large-vocabulary speech recognition task demonstrates
significant and consistent error rate reduction using these
techniques. The resulting error rate is shown to be lower than
that achieved by the matched-noisy condition for both
stationary and nonstationary natural, as well as simulated,
noises.

‚Äč