We propose an efficient new method for estimating scaling factors among probabilistic models in speech recognition. Most speech recognition systems consist of an acoustic and a language model, and require scaling factors to balance probabilities among them. The scaling factors are conventionally optimized in recognition tests. In our proposed method, the scaling factors are regarded as parameters of a log-linear model, and they are estimated using a gradient-ascent method based on the
maximum a posteriori probability criterion. Posterior probability is computed using word-lattices. We employ an iteration technique which repeats a word-lattice-generation/scaling-factor-estimation process, and the resulting scaling factor estimation is robust with respect to the changes in initial values. In experiments, estimated scaling factors were nearly identical to optimal values obtained in a greedy grid search, and they changed little with variations in initial values.
Bibliographic reference. Emori, Tadashi / Onishi, Yoshifumi / Shinoda, Koichi (2007): "Automatic estimation of scaling factors among probabilistic models in speech recognition", In INTERSPEECH-2007, 1453-1456.