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In this paper, we present an end-to-end language identification framework,the attention-based Convolutional Neural Network-Bidirectional Long-short TermMemory (CNN-BLSTM). The model is performed on the utterance level, which meansthe utterance-level decision can be directly obtained from the output of theneural network. To handle speech utterances with entire arbitrary andpotentially long duration, we combine CNN-BLSTM model with a self-attentivepooling layer together. The front-end CNN-BLSTM module plays a role as localpattern extractor for the variable-length inputs, and the followingself-attentive pooling layer is built on top to get the fixed-dimensionalutterance-level representation. We conducted experiments on NIST LRE07closed-set task, and the results reveal that the proposed attention-basedCNN-BLSTM model achieves comparable error reduction with other state-of-the-artutterance-level neural network approaches for all 3 seconds, 10 seconds, 30seconds duration tasks.

Utterance-level end-to-end language identification using attention-based CNN-BLSTM