DuDe: Dual-Decoder Multilingual ASR for Indian Languages using Common Label Set

Autor:	A, Arunkumar, Batra, Mudit, S, Umesh
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Computation and Language Computer Science - Machine Learning Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	In a multilingual country like India, multilingual Automatic Speech Recognition (ASR) systems have much scope. Multilingual ASR systems exhibit many advantages like scalability, maintainability, and improved performance over the monolingual ASR systems. However, building multilingual systems for Indian languages is challenging since different languages use different scripts for writing. On the other hand, Indian languages share a lot of common sounds. Common Label Set (CLS) exploits this idea and maps graphemes of various languages with similar sounds to common labels. Since Indian languages are mostly phonetic, building a parser to convert from native script to CLS is easy. In this paper, we explore various approaches to build multilingual ASR models. We also propose a novel architecture called Encoder-Decoder-Decoder for building multilingual systems that use both CLS and native script labels. We also analyzed the effectiveness of CLS-based multilingual systems combined with machine transliteration. Comment: Submitted to ICASSP 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.16739 Zobrazit plný text záznamu View this record from Arxiv