A Recurrent Neural Networks (RNN) based attention model has been used in code-switching speech recognition (CSSR). However, due to the sequential computation constraint of RNN, there are stronger short-range dependencies and weaker long-range dependencies, which makes it hard to immediately switch languages in CSSR. Firstly, to deal with this problem, we introduce the CTC-Transformer, relying entirely on a self-attention mechanism to draw global dependencies and adopting connectionist temporal classification (CTC) as an auxiliary task for better convergence. Secondly, we proposed two multi-task learning recipes, where a language identification (LID) auxiliary task is learned in addition to the CTC-Transformer automatic speech recognition (ASR) task. Thirdly, we study a decoding strategy to combine the LID into an ASR task. Experiments on the SEAME corpus demonstrate the effects of the proposed methods, achieving a mixed error rate (MER) of 30.95%. It obtains up to 19.35% relative MER reduction compared to the baseline RNN-based CTC-Attention system, and 8.86% relative MER reduction compared to the baseline CTC-Transformer system.
Decoupling Pronunciation and Language for End-to-End Code-Switching Automatic Speech Recognition