|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
INVESTIGATION OF ACOUSTIC MODELING TECHNIQUES FOR LVCSR SYSTEMS
X. Liu, M. J. F. Gales, K. C. Sim and K. Yu
This paper describes the use of several advanced acoustic modeling techniques for the 2004 CU-HTK large vocabulary speech recognition systems. These techniques include Gaussianization for speaker normalization, discriminative Cluster Adaptive Training (CAT), Subspace for Precision And Mean (SPAM) modeling of inverse covariances, and discriminative complexity control. Acoustic models featuring these techniques were integrated into a state-of-the-art 10 real-time multi-pass system with sophisticated adaptation for performance evaluation. Experimental results are presented on both broadcast news (BN) and conversational telephone speech (CTS) transcription tasks.
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2005 Cambridge University Engineering Dept
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