Combining recurrent neural networks and factored language models during decoding of code-Switching speech

Abstract

In this paper, we present our latest investigations of language modeling for Code-Switching. Since there is only little text material for Code-Switching speech available, we integrate syntactic and semantic features into the language modeling process. In particular, we use part-of-speech tags, language identifiers, Brown word clusters and clusters of open class words. We develop factored language models and convert recurrent neural network language models into backoff language models for an efficient usage during decoding. A detailed error analysis reveals the strengths and weaknesses of the different language models. When we interpolate the models linearly, we reduce the perplexity by 15.6% relative on the SEAME evaluation set. This is even slightly better than the result of the unconverted recurrent neural network. We also combine the language models during decoding and obtain a mixed error rate reduction of 4.4% relative on the SEAME evaluation set.