  In this paper we proposed an end-to-end short utterances speech language
identification(SLD) approach based on a Long Short Term Memory (LSTM) neural
network which is special suitable for SLD application in intelligent vehicles.
Features used for LSTM learning are generated by a transfer learning method.
Bottle-neck features of a deep neural network (DNN) which are trained for
mandarin acoustic-phonetic classification are used for LSTM training. In order
to improve the SLD accuracy of short utterances a phase vocoder based
time-scale modification(TSM) method is used to reduce and increase speech rated
of the test utterance. By splicing the normal, speech rate reduced and
increased utterances, we can extend length of test utterances so as to improved
improved the performance of the SLD system. The experimental results on
AP17-OLR database shows that the proposed methods can improve the performance
of SLD, especially on short utterance with 1s and 3s durations.
