Akinori Ito

Language modeling by string pattern N-gram for Japanese speech recognition

This paper describes a new powerful statistical language model based on N-gram model for Japanese speech recognition. In English, a sentence is written word-by-word. On the other hand. A sentence in Japanese has no word boundary character. Therefore. A Japanese sentence requires word segmentation by morphemic analysis before the construction of word N-gram. We propose an N-gram based language model which requires no word segmentation. This model uses character string patterns as units of N-gram. The string patterns are chosen from the training text according to a statistical criterion. We carried out several experiments to compare perplexities of the proposed and the conventional models. which showed the advantage of our model. For many of the readers interest, we applied this method to English text. As the result of a preliminary experiment, the proposed method got better performance than conventional word trigram.

Construction and evaluation of language models based on stochastic context-free grammar for speech recognition

This paper deals with the use of a stochastic context-free grammar (SCFG) for large vocabulary continuous speech recognition; in particular, an SCFG with phrase-level dependency rules is built. Unlike n-gram models, the SCFG can describe not only local constraints but also global constraints pertaining to the sentence as a whole, thus making possible language models with great expressive power. However, the inside-outside algorithm must be used for estimation of the SCFG parameters, which involves a great amount of calculation, proportional to the third power of the number of nonterminal symbols and of the input string length. Hence, due to problems in dealing with extensive text corpora, the SCFG has hardly been applied as a language model for very large vocabulary continuous speech recognition. The proposed phrase-level dependency SCFG allows a significant reduction of the computational load. In experiments with the EDR corpus, the proposed method proved effective. In experiments with the Mainichi corpus, a large-scale phrase-level dependency SCFG was built for a very large vocabulary continuous speech recognition system. Speech recognition tests with a vocabulary of about 5000 words showed that the proposed method could not compare with the trigram model in performance; however, when it was used in combination with a trigram model, the error rate was reduced by 14% compared to the trigram model alone.