Phonetic Temporal Neural Model for Language Identification
This work addresses language identification for speech processing applications, offering incremental improvements by integrating phonetic knowledge into neural models.
The paper tackles language identification by incorporating phonetic information into an LSTM-RNN model, using frame-level phonetic features from a phone-discriminative DNN, and demonstrates effectiveness with significant performance improvements over existing acoustic neural models and the i-vector approach on short utterances and in noisy conditions.
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: it is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.