  Code-switching speech recognition has attracted an increasing interest
recently, but the need for expert linguistic knowledge has always been a big
issue. End-to-end automatic speech recognition (ASR) simplifies the building of
ASR systems considerably by predicting graphemes or characters directly from
acoustic input. In the mean time, the need of expert linguistic knowledge is
also eliminated, which makes it an attractive choice for code-switching ASR.
This paper presents a hybrid CTC-Attention based end-to-end Mandarin-English
code-switching (CS) speech recognition system and studies the effect of hybrid
CTC-Attention based models, different modeling units, the inclusion of language
identification and different decoding strategies on the task of code-switching
ASR. On the SEAME corpus, our system achieves a mixed error rate (MER) of
34.24%.
