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- W2124008567 abstract "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. 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." @default.
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- W2124008567 title "Variable n-grams and extensions for conversational speech language modeling" @default.
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- W2124008567 doi "https://doi.org/10.1109/89.817454" @default.
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