On the use of phone gram units in recurrent neural networks for language identification
In this paper we present our results on using RNN ba sed LM scores trained on different phone gram orders and u sing different phonetic ASR recognizers. In order to avoi d data sparseness problems and to reduce the vocabulary of all possible n gram combinations, a K means clustering procedure was performed using phone vector embeddin gs as a pre processing step. Additional experiments to opti mize the amount of classes, batch size, hidden neurons, stat e unfolding, are also presented. We have worked with the KALAKA3 database for the plenty closed condition [1] Thank s to our clustering technique and the combination of high le vel phonegrams, our phonotactic system performs ~13% better than the unigram based RNNLM system. Also, the obtained RNNLM scores are calibrated and fused with other scores f rom an acoustic based i vector system and a traditional PP RLM system. This fusion provides additional improvement s showing that they provide complementary information t the LID system.
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