Language identification on code-switching utterances using multiple cues

Abstract

Code-switching speech is an utterance containing two or more languages. Usually, the switching linguistic unit is in clause or word levels. In this paper, a two-stage framework is proposed, containing a language identifier and then a speech recognizer, to evaluate on a Mandarin-Taiwanese codeswitching utterance. In the language identifier, we use multiple cues including acoustic, prosodic and phonetic features. In order to integrate the cues to distinguish one language from another, we used a maximum a posteriori decision rule to connect an acoustic model, a duration model and a language model. In the experiments, we have achieved 34.5% (LID) and 17.7% (ASR) error rate reduction comparing with one stage LVCSR-based system.