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 (SpeechDat(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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: David Imseng, Hervé Bourlard, Mathew Magimai-Doss, John Dines
• Keywords: ICASSP 2011, Mixed Language, Posterior Probabilities, Signal Processing, Speech Recognition