An Error-correct Approach in Chinese Automatic Speech Recognition Based on Semantic Analysis
Wei Xiang · 2006
Now automatic speech recognition (ASR) is not a simplex signal processing. The natural language processing is more and more regarded in Chinese ASR. As a language model, N-gram improved the accurate rate and stability of ASR remarkably. But there are still many syntactic and semantic errors in ASR because of the inherent limitation of N-gram language model. This paper analysed the reson and the types of the phonetic and literal errors in ASR. An error-correct approach in Chinese ASR was proposed in this paper based on sentence semantic analysis, confusion matrix and a language model constructed on hierarchical network of concepts. The error-correct software system runs well especially in correctting the errors of semantic relationship, tested with vocal corpus of 3 person and 50,000 words and with 216 experimental sentences for error-correct. So the new language model constructed on hierarchical network of concepts can overcome the limitation of N-gram model. The approach in this paper also can be merged into ASR to improve the performance of error-correct in ASR.