Similar n-gram language model

This paper describes an extension of the n-gram language model: the similar n-gram language model. The estimation of the probability P (s) of a string s by the classical model of order n is computed using statistics of occurrences of the last nwords of the string in the corpus, whereas the proposed model further uses all the strings s′ for which the Levenshtein distance to s is smaller than a given threshold. The similarity between s and each string s′ is estimated using co-occurrence statistics. The new P (s) is approximated by smoothing all the similar n-gram probabilities with a regression technique. A slight but statistically significant decrease in the word error rate is obtained on a state-of-the-art automatic speech recognition system when the similar n-gram language model is interpolated linearly with the n-gram model.