Code switch language modeling with Functional Head Constraint

In this paper, we propose for the first time to incorporate the linguistically well-known Functional Head Constraint into a code switch language model for speech recognition. Under this constraint, code switch cannot occur between the functional head and its complements. The constrained code switching language model is obtained by first expanding the search network with a translation model; and then using lattice-based parsing to restrict paths to those permissible under the Functional Head Constraint. We tested our system on two tasks of code switch speech recognition, namely lecture speech recognition and lunch conversation recognition. Our system reduces word error rates (WER) from a baseline mixed language model by 3.72% relative in the first task; and by 5.85% in the second task. It reduces WER from an interpolated language model by 2.51% in the first task; and by 4.57% in the second task. All results are statistically significant. In addition, our method reduces WER for both the matrix language and the embedded language.