Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41823
Title: Probabilistic state clustering using conditional random field for context-dependent acoustic modelling
Authors: Sim, K.C. 
Keywords: Complexity control
Conditional random field
State clustering
Issue Date: 2010
Source: Sim, K.C. (2010). Probabilistic state clustering using conditional random field for context-dependent acoustic modelling. Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010 : 70-73. ScholarBank@NUS Repository.
Abstract: Hidden Markov Models are widely used in speech recognition systems. Due to the co-articulation effects of continuous speech, context-dependent models have been found to yield performance improvements. One major issue with context-dependent acoustic modelling is the robust parameter estimation of unseen or rare models in the training data. Typically, decision tree state clustering is used to ensure that there are sufficient data for each physical state. Decision trees based on phonetic questions are used to cluster the states. In this paper, conditional random field (CRF) is used to perform probabilistic state clustering where phonetic questions are used as binary feature functions to predict the latent cluster weights. Experimental results on the Wall Street Journal reveals that CRF-based state clustering outperformed the conventional maximum likelihood decision tree state clustering with similar model complexities by about 10% relative. © 2010 ISCA.
Source Title: Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/41823
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