Investigating End-to-end Speech Recognition for Mandarin-english Code-switching

Abstract

Code-switching is a common phenomenon in many multilingual communities and presents a challenge to automatic speech recognition (ASR). In this paper, three approaches are investigated to improve end-to-end speech recognition on Mandarin-English code-switching task. First, multi-task learning (MTL) is introduced which enables the language identity information to facilitate Mandarin-English code-switching ASR. Second, we explore wordpieces, as opposed to graphemes, as English modeling units to reduce the mod-eling unit gap between Mandarin and English. Third, we employ transfer learning to utilize larger amount of monolingual Mandarin and English data to compensate the data sparsity issue of a code-switching task. Significant improvements are observed from all three approaches. With all three approaches combined, the final system achieves a character error rate (CER) of 6.49% on a real Mandarin-English code-switching task.