Incremental on-line feature space MLLR adaptation for telephony speech recognition

Abstract

In this paper, we present a method for incremental on-line adaptation based on feature space Maximum Likelihood Linear Regression (FMLLR) for telephony speech recognition applications. We explain how to incorporate a feature space MLLR transform into a stack decoder and perform on-line adaptation. The issues discussed are as follows: collecting adaptation data on-line and in real time; mapping adaptation data from previous feature space to the present feature space; and smoothing adaptation statistics with initial statistics based on original acoustical model to achieve stability. Testing results on various systems demonstrate that on-line incremental FM-LLR adaptation could be an effective and stable method when the adaptation statistics are mapped and smoothed.