what results do they achieve?
 The relative accuracy (ACC) of the two multi-stage systems increases by 10% on every task (88.88 and 87.24)  relative to the baseline  78.85.
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.
The aim of language identification (LID) is to determine the language of an utterance and can be defined as a variable-length sequence classification task on the utterance-level. The task introduced in this paper is more challenging than general LID tasks cause we use a dialect database which contains 10 dialects in China. The dialects' regions are close to each other and they all belong to Chinese, so they have the same characters and similar pronunciations. Recently, the use of deep neural network (DNN) has been explored in LID tasks. The DNN is trained to discriminate individual physical states of a tied-state triphone and then extract the bottleneck features to a back-end system for classification BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . End-to-end frameworks based on DNN later are trained for LID BIBREF4 . Other network architectures are successfully applied to LID task, example for convolutional neural network (CNN) BIBREF5 , BIBREF6 , time delay neural network (TDNN) BIBREF7 , RNN BIBREF8 , BIBREF9 , BIBREF10 , and BIBREF11 has a CNN followed by an RNN structure, which is similar to ours. They predict the final category of an utterance directly by the last fully connected layer, or derive the results by averaging the the frame-level posteriors. These frameworks just trained end-to-end to recognize languages, but they do not consider the phonetic information concretely. On the other hand, in many utterance analyzing tasks such as acoustic speech recognition (ASR), speaker verification (SV) and our LID, only a simple task or a specific aim is focused on. However, an utterance always has multi-dimensional information such as content, emotion, speaker and language and there are some certain correlations between them. Although the LID task is text-independent, which means the content of each utterance is totally different, different languages may have its own pronunciations or tones. Thus acoustic and language are two components in the LID task, BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 use the bottleneck features from an ASR system and feed to another neural network for recognition. Nevertheless, these ASR DNNs constituted by fully connected layers adds significant computational complexity and also require labels of physical states of a tied-state triphone. Inspired by all this, we assume that the high-dim features extracted from the network will contain information of pronunciation and language category, so we combine the ASR method with our LID task and here are our main contributions: The remainder of the paper is organized as follows. Section 2 introduces some related works about ASR and section 3 introduces the ResNet14 structure and gives processing of the two multi-stage systems. We present details of the database and initialization methods of the network in Section 4. The results of experiments and analysis are shown in Section 5. Lastly we give the conclusion and some future work in Section 6.