Neural network approach to adaptive learning: with an application to Chinese homophone disambiguation

Abstract

Contextual language processing plays an important role for the post-processing of speech recognition. The purpose of the contextual language processing is to find the most plausible candidate for each syllable with the maximum likelihood probability. Generally, the performance of the probabilistic model is affected by two major errors, i.e., modeling error and estimation error in training corpus. In this paper, we focus on the problem of estimation error in training corpus. An adaptive learning algorithm is proposed to decrease the influences of variant run-time context domain. It shows which objects are to be adjusted and how to adjust their probabilities by a neural network model. The resulting techniques are greatly simplified and robust. The experimental results demonstrate the effects of the learning algorithm from generic domain to specific domain. In general, these techniques can be easily extended to various language models and corpus-based applications.