A Divide-and-Conquer Approach for Language Identification Based on Recurrent Neural Networks

This paper describes the design of an acoustic language recognition system based on BLSTM that can discriminate closely related languages and dialects of the same language. We introduce a Divide-and-Conquer (D&C) method to quickly and successfully train an RNN-based multi-language classifier. Experiments compare this approach to the straightforward training of the same RNN, as well as to two widely used LID techniques: a phonotactic system using DNN acoustic models and an i-vector system. Results are reported on two different data sets: the 14 languages of NIST LRE07 and the 20 closely related languages and dialects of NIST OpenLRE15. In addition to reporting the NIST Cavg metric which served as the primary metric for the LRE07 and OpenLRE15 evaluations, the EER and LER are provided. When used with BLSTM, the D&C training scheme significantly outperformed the classical training method for multi-class RNNs. On the OpenLRE15 data set, this method also outperforms classical LID techniques and combines very well with a phonotactic system.