Recurrent neural network language modeling for code switching conversational speech

Code-switching is a very common phenomenon in multilingual communities. In this paper, we investigate language modeling for conversational Mandarin-English code-switching (CS) speech recognition. First, we investigate the prediction of code switches based on textual features with focus on Part-of-Speech (POS) tags and trigger words. Second, we propose a structure of recurrent neural networks to predict code-switches. We extend the networks by adding POS information to the input layer and by factorizing the output layer into languages. The resulting models are applied to our task of code-switching language modeling. The final performance shows 10.8% relative improvement in perplexity on the SEAME development set which transforms into a 2% relative improvement in terms of Mixed Error Rate and a relative improvement of 16.9% in perplexity on the evaluation set which leads to a 2.7% relative improvement of MER.