Web-based possibilistic language models for automatic speech recognition

This paper describes a new kind of language models based on the Possibility Theory. The purpose of these new models is to better use the data available on the Web for language modeling. These models aim to integrate information relative to impossible word sequences. We address the two main problems of using this kind of model: how to estimate the measures for word sequences and how to integrate this kind of model into the ASR system. We propose a word-sequence possibilistic measure and a practical estimation method based on word-sequence statistics, which is particularly suited for estimating from Web data. We develop several strategies and formulations for using these models in a classical automatic speech recognition engine, which relies on a probabilistic modeling of the speech recognition process. This work is evaluated on two typical usage scenarios: broadcast news transcription with very large training sets, and transcription of medical videos, in a specialized domain, with only very limited training data. The results show that the possibilistic models provide significantly lower word error rate on the specialized domain task, where classical n-gram models fail due to the lack of training materials. For the broadcast news, the probabilistic models remain better than the possibilistic ones. However, a log-linear combination of the two kinds of models outperforms all the models used individually, which indicates that possibilistic models bring information that is not modeled by probabilistic ones.