Dual Script E2E Framework for Multilingual and Code-Switching ASR

India is home to multiple languages, and training automatic speech recognition (ASR) systems is challenging. Over time, each language has adopted words from other languages, such as English, leading to code-mixing. Most Indian languages also have their own unique scripts, which poses a major limitation in training multilingual and code-switching ASR systems. Inspired by results in text-to-speech synthesis, in this paper, we use an in-house rule-based phoneme-level common label set (CLS) representation to train multilingual and code-switching ASR for Indian languages. We propose two end-to-end (E2E) ASR systems. In the first system, the E2E model is trained on the CLS representation, and we use a novel data-driven back-end to recover the native language script. In the second system, we propose a modification to the E2E model, wherein the CLS representation and the native language characters are used simultaneously for training. We show our results on the multilingual and code-switching (MUCS) ASR challenge 2021. Our best results achieve approximate to 6% and 5% improvement in word error rate over the baseline system for the multilingual and code-switching tasks, respectively, on the challenge development data.