M. H. Siu and M. Ostendorf,
''Variable N-grams and Extensions for Conversational Speech Language Modeling,''
IEEE Transactions on Speech and Audio Processing, vol. 8, no. 1, pp 63-75.

Recent progress in variable n-gram language modeling provides an efficient representation of n-gram models and makes training of higher order n-grams possible. In this paper, we apply the variable n-gram design algorithm to conversational speech, extending the algorithm to learn skips and context-dependent classes to handle conversational speech characteristics such as filler words, repetitions and other disfluencies. Experiments show that using the extended variable n-gram results in a language model that captures 4-gram context with less than half the parameters of a standard trigram while also improving the test perplexity and recognition accuracy.

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