Universal background model based speech recognition
Daniel Povey, Stephen Mingyu Chu, Balakrishnan Varadarajan · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
Theuniversalbackgroundmodel(UBM) is an effective framework widely used in speaker recognition. But so far it has received little attention from the speech recognition field. In this work, we make a first attempt to apply the UBM to acoustic modeling in ASR. We propose a tree-based parameter estimation technique for UBMs, and describe a set of smoothing and pruning methods to facilitate learning. The proposed UBM approach is benchmarked on a state-of-the-art large-vocabulary continuous speech recognition platform on a broadcast transcription task. Preliminary experiments reported in this paper already show very exciting results.