Efficient search with posterior probability estimates in HMM-based speech recognition

In this paper we present the methods we developed to estimate posterior probabilities for HMM states in continuous and discrete HMM-based speech recognition systems and several ways to speed up decoding by using these posterior probability estimates. The proposed pruning techniques are state deactiv...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Willett, D., Neukirchen, C., Rigoll, G.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Computer science Context modeling Decoding Distribution functions Hidden Markov models Laplace equations Neural networks Speech recognition State estimation
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