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Adding new words into a language model using parameters of known words with similar behavior

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Abstract

This article presents a study on how to automatically add new words into a language model without retraining it or adapting it (which requires a lot of new data). The proposed approach consists in finding a list of similar words for each new word to be added in the language model. Based on a small set of sentences containing the new words and on a set of n-gram counts containing the known words, we search for known words which have the most similar neighbor distribution (of the few preceding and few following neighbor words) to the new words. The similar words are determined through the computation of KL divergences on the distribution of neighbor words. The n-gram parameter values associated to the similar words are then used to define the n-gram parameter values of the new words. In the context of speech recognition, the performance assessment on a LVCSR task shows the benefit of the proposed approach.
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Dates and versions

hal-01184194 , version 1 (13-08-2015)

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  • HAL Id : hal-01184194 , version 1

Cite

Luiza Orosanu, Denis Jouvet. Adding new words into a language model using parameters of known words with similar behavior. International Conference on Natural Language and Speech Processing, Oct 2015, Alger, Algeria. ⟨hal-01184194⟩
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