Reducing Language Confusion for Code-Switching Speech Recognition with Token-Level Language Diarization

Abstract

Code-switching (CS) occurs when languages switch within a speech signal and leads to language confusion for automatic speech recognition (ASR). We address the problem of language confusion for improving CS-ASR from two perspectives: incorporating and disentangling language information. We incorporate language information within the CS-ASR model by dynamically biasing the model with token-level language posteriors corresponding to outputs of a sequence-to-sequence auxiliary language diarization (LD) module. In contrast, the disentangling process reduces the difference between languages via adversarial training so as to normalize two languages. We conduct experiments on the SEAME dataset. Compared to the baseline model, both the joint optimization with LD and the language posterior bias achieve performance improvement. Comparison of the proposed methods indicates that incorporating language information is more effective than disentangling for reducing language confusion in CS speech.