Language model and acoustic model information in probabilistic speech recognition

The authors propose an approach to the estimation of the performance of the language model and the acoustic model in probabilistic speech recognition that tries to take into account the interaction between the two. It consists of a new measure, called speech decoder entropy (SDE), of joint acoustic-context information. Some results are presented for a 20000-word vocabulary recognizer. The authors discuss some limitations of the work and some suggestions for future developments