Combination of Recurrent Neural Networks and Factored Language Models for Code-Switching Language Modeling
Heike Adel, Ngoc Thang Vu, Tanja Schultz · 2013
In this paper, we investigate the application of recurrent neural network language models (RNNLM) and factored language models (FLM) to the task of language modeling for Code-Switching speech. We present a way to integrate partof-speech tags (POS) and language information (LID) into these models which leads to significant improvements in terms of perplexity. Furthermore, a comparison between RNNLMs and FLMs and a detailed analysis of perplexities on the different backoff levels are performed. Finally, we show that recurrent neural networks and factored language models can be combined using linear interpolation to achieve the best performance. The final combined language model provides 37.8% relative improvement in terms of perplexity on the SEAME development set and a relative improvement of 32.7 % on the evaluation set compared to the traditional n-gram language model. Index Terms: multilingual speech processing, code switching, language modeling, recurrent neural networks, factored language models 1