Tree-Based State Tying for High Accuracy Modelling

The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance between model complexity and available training data. For large vocabulary systems requiring cross-word context dependent modelling, this is particularly acute since many mmh contexts will never occur in the training data. This paper describes a method of creating a tied-state continuous speech recognition system using a phonetic decision tree. This treebased clustering is shown to lead to similar recognition performance to that obtained using an earlier data-driven approach but to have the additional advantage of providing a mapping for unseen triphones. State-tying is also compared with traditional model-based tying and shown to be clearly superior. Experimental results are presented for both the Resource Management and Wall Street 3ournal tasks.

• Type: Conference
• Year: 1994
• Where: NAACL
• Authors: S. J. Young, J. J. Odell, Philip C. Woodland
• Keywords: Continuous Speech, HMM-based Continuous Speech, NAACL 1994, NAACL 2007, Training Data