Asymmetric acoustic modeling of mixed language speech

We propose to improve speech recognition performance on speaker-independent, mixed language speech by asymmetric acoustic modeling. Mixed language is either inter-sentential code switching from the source matrix language to a foreign language or intra-sentential code mixing between the matrix language and embedded foreign words or phrases. In either case, the foreign phrases are pronounced by the matrix language speaker with varying degrees of accent. Our pro posed system using selective decision tree merging between a bilingual model and an accented embedded speech model outperforms previous approaches of either using a bilingual model with model retraining by 21.51%, or using adaptation by 15.88%. It outperforms all models on both code mixing and code switching cases. We successfully improved recognition on embedded foreign speech without degrading the performance on the matrix language speech.