  In this paper, we present a two-stage language identification (LID) system
based on a shallow ResNet14 followed by a simple 2-layer recurrent neural
network (RNN) architecture, which was used for Xunfei (iFlyTek) Chinese Dialect
Recognition Challenge and won the first place among 110 teams. The system
trains an acoustic model (AM) firstly with connectionist temporal
classification (CTC) to recognize the given phonetic sequence annotation and
then train another RNN to classify dialect category by utilizing the
intermediate features as inputs from the AM. Compared with a three-stage system
we further explore, our results show that the two-stage system can achieve high
accuracy for Chinese dialects recognition under both short utterance and long
utterance conditions with less training time.
