Automatically Finding Semantically Consistent N-grams to Add New Words in LVCSR Systems

Gwénolé Lecorvé (1) , Guillaume Gravier (1) , Pascale Sébillot (1)

1 TEXMEX - Multimedia content-based indexing

This paper presents a new method to automatically add n-grams containing out-of-vocabulary (OOV) words to a baseline language model (LM), where these n-grams are sought to be grammatically correct and to make sense according to the meaning of OOV words. First, this method consists in determining the word sequences, i.e., n-grams, in which the usage of a given OOV word is the most semantically consistent. Then, conditional probabilities of these n-grams have to be computed. To do this, semantic relations between words are used to assimilate each OOV word to several equivalent in-vocabulary words. Based on these last words, n-grams from the baseline LM are re-used to find the word sequences to be added and to compute their probabilities. After augmenting the vocabulary and launching a recognition process, experiments show that our method results in WER improvements which are comparable to those obtained using a state-of-the-art open vocabulary LM.

Mots clés: Automatic speech recognition; Vocabulary adaptation; Natural language processing; Language modeling

Domaines: Traitement du texte et du document

Gwénolé Lecorvé, Guillaume Gravier, Pascale Sébillot. Automatically Finding Semantically Consistent N-grams to Add New Words in LVCSR Systems. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, May 2011, Prague, Czech Republic. 4 p., 2 columns. ⟨hal-00645223⟩