Automatic language identification with perceptually guided training and recurrent neural networks

We present a novel approach to Automatic Language Identification (LID). We propose Perceptually Guided Training (PGT), a novel LID training method, involving identification of utterance parts which are particularly significant perceptually for the language identification process, and exploitation of these Perceptually Significant Regions (PSRs) to guide the LID training process. Our approach involves a Recurrent Neural Network (RNN) as the main mechanism. We propose that, because of the long-range intrautterance acoustical context significance in LID, RNNs are particularly suitable for the LID task. Our approach does not require phonetic labeling or transcription of the training corpus. LIREN/PGT, the LID system we developed, incorporates our approach. Our LID experiments were on English, German, and Mandarin Chinese, using the OGI-TS corpus.