Experimental evaluation of Italian language models for large-dictionary speech recognition

This paper reports on experiments performed on the ltalian language in order to assess the efficiency of probabilistic language models with reference to a task of large-dictionary speech recognition. Two different types of models, an M -gram and an Mg-gram one, have been investigated for comparison purposes. The quality of the models trained on a corpus of 3.5 million words was measured in terms · of perplexity and of the improvement achieved by integrating the language model in real speech recognition systems. Judging from this empirical measurement, the two language models exhibit equivalent preformance for ltalian, although perplexity measurements would suggest otherwise.