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.
Return to SSLI Lab Publications