Dual Script E2E framework for Multilingual and Code-Switching ASR

• URL: http://arxiv.org/abs/2106.01400v1
• Date: Wed, 2 Jun 2021 18:08:27 GMT
• Title: Dual Script E2E framework for Multilingual and Code-Switching ASR
• Authors: Mari Ganesh Kumar, Jom Kuriakose, Anand Thyagachandran, Arun Kumar A, Ashish Seth, Lodagala Durga Prasad, Saish Jaiswal, Anusha Prakash, Hema Murthy
• Abstract summary: We train multilingual and code-switching ASR systems for Indian languages. Inspired by results in text-to-speech synthesis, we use an in-house rule-based common label set ( CLS) representation. We show our results on the multilingual and code-switching tasks of the Indic ASR Challenge 2021.
• Score: 4.697788649564087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: India is home to multiple languages, and training automatic speech recognition (ASR) systems for languages 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 work, 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 tasks of the Indic ASR Challenge 2021. Our best results achieve 6% and 5% improvement (approx) in word error rate over the baseline system for the multilingual and code-switching tasks, respectively, on the challenge development data.

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