Automatic rule extraction for modeling pronunciation variation

Abstract

This paper describes the technique for automatic extraction of pronunciation rules from continuous speech corpus. The purpose of the work is to model pronunciation variation in phoneme based continuous speech recognition at language model level. In modeling pronunciation variations, morphological variations and out-of-vocabulary words problem are also implicitly modeled in the system. It is not possible to model these kind of variations using dictionary based approach in phoneme based automatic speech recognition. The variations are automatically learned from annotated continuous speech corpus. The corpus is first aligned, on the basis of phoneme and letter, using a dynamic string alignment algorithm. The DSA is applied to isolated words to deal with intra-word variations as well as to complete sentences in the corpus to deal with inter-word variations. The pronunciation rules phonemes→letters are extracted from these aligned speech units to build pronunciation model. The rules are finally fed to a phoneme-to-word decoder for recognition of the words having different pronunciations or that are OOV. © 2011 Springer-Verlag.