Recently, language identity information has been utilized to improve the performance of end-to-end code-switching (CS) speech recognition task. However, previous work use an additional language identification (LID) model as an auxiliary module, which increases computation cost. In this work, we propose an improved recurrent neural network transducer (RNN-T) model with language bias to alleviate the problem. We use the language identities to bias the model to predict the CS points. This promotes the model to learn the language identity information directly from transcriptions, and no additional LID model is needed. We evaluate the approach on a Mandarin-English CS corpus SEAME. Compared to our RNN-T baseline, the RNN-T with language bias can achieve 16.2% and 12.9% relative mixed error reduction on two test sets, respectively.