  Modeling unit and model architecture are two key factors of Recurrent Neural
Network Transducer (RNN-T) in end-to-end speech recognition. To improve the
performance of RNN-T for Mandarin speech recognition task, a novel transformer
transducer with the combination architecture of self-attention transformer and
RNN is proposed. And then the choice of different modeling units for
transformer transducer is explored. In addition, we present a new mix-bandwidth
training method to obtain a general model that is able to accurately recognize
Mandarin speech with different sampling rates simultaneously. All of our
experiments are conducted on about 12,000 hours of Mandarin speech with
sampling rate in 8kHz and 16kHz. Experimental results show that Mandarin
transformer transducer using syllable with tone achieves the best performance.
It yields an average of 14.4% and 44.1% relative Word Error Rate (WER)
reduction when compared with the models using syllable initial/final with tone
and Chinese character, respectively. Also, it outperforms the model based on
syllable initial/final with tone with an average of 13.5% relative Character
Error Rate (CER) reduction.
