Probabilistic and possibilistic language models based on the world wide web

In a previous paper we proposed Web-based language models relying on the possibility theory. These models explicitly repre-sent the possibility of word sequences. In this paper we propose to find the best way of combining this kind of model with clas-sical probabilistic models, in the context of automatic speech recognition. We propose several combination approaches, de-pending on the nature of the combined models. With respect to the baseline, the best combination provides an absolute word error rate reduction of about 1 % on broadcast news transcrip-tion, and of 3.5 % on domain-specific multimedia document transcription. Index Terms: language models, world wide web, possibility measure, automatic speech recognitio