Language Modeling with Functional Head Constraint for Code Switching Speech Recognition

Ying Li, Pascale Fung

Abstract: In this paper, we propose novel structured language modeling methods for code mixing speech recognition by incorporating a well-known syntactic constraint for switching code, namely the Functional Head Constraint (FHC). Code mixing data is not abundantly available for training language models. Our proposed methods successfully alleviate this core problem for code mixing speech recognition by using bilingual data to train a structured language model with syntactic constraint. Linguists and bilingual speakers found that code switch do not happen between the functional head and its complements. We propose to learn the code mixing language model from bilingual data with this constraint in a weighted finite state transducer (WFST) framework. The constrained code switch language model is obtained by first expanding the search network with a translation model, and then using parsing to restrict paths to those permissible under the constraint. We im