LANGUAGE DEPENDENT UNIVERSAL PHONEME POSTERIOR ESTIMATION FOR MIXED LANGUAGE SPEECH RECOGNITION

This paper presents a new approach to estimate "universal" phoneme posterior probabilities for mixed language speech recognition. More specifically, we propose a new theoretical framework to combine phoneme class posterior probabilities in a principled way by using (statistical) evidence about the language identity. We investigate the proposed approach in a mixed language environment (Speech-Dat(II)) consisting of five European languages. Our studies show that the proposed approach can yield significant improvements on a mixed language task, while maintaining the performance on monolingual tasks. Additionally, through a case study, we also demonstrate the potential benefits of the proposed approach for non-native speech recognition.