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A tree-based statistical language model for natural language speech recognition

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4 Author(s)
Bahl, Lalit R. ; IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA ; Brown, P. ; de Souza, P.V. ; Mercer, R.

The problem of predicting the next word a speaker will say, given the words already spoken; is discussed. Specifically, the problem is to estimate the probability that a given word will be the next word uttered. Algorithms are presented for automatically constructing a binary decision tree designed to estimate these probabilities. At each node of the tree there is a yes/no question relating to the words already spoken, and at each leaf there is a probability distribution over the allowable vocabulary. Ideally, these nodal questions can take the form of arbitrarily complex Boolean expressions, but computationally cheaper alternatives are also discussed. Some results obtained on a 5000-word vocabulary with a tree designed to predict the next word spoken from the preceding 20 words are included. The tree is compared to an equivalent trigram model and shown to be superior

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Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:37 ,  Issue: 7 )