Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model

Citations Over TimeTop 10% of 2019 papers

Abstract

Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages.They have shown improvement over monolingual systems, and have simplified training and serving by eliminating language-specific acoustic, pronunciation, and language models.This work presents an E2E multilingual system which is equipped to operate in low-latency interactive applications, as well as handle a key challenge of real world data: the imbalance in training data across languages.Using nine Indic languages, we compare a variety of techniques, and find that a combination of conditioning on a language vector and training language-specific adapter layers produces the best model.The resulting E2E multilingual model achieves a lower word error rate (WER) than both monolingual E2E models (eight of nine languages) and monolingual conventional systems (all nine languages).

Related Papers

• A datasieve based audiovisual continuous speech recognition system(2003)
• → AUTOMATIC SPEECH RECOGNITION APPROACH FOR DIVERSE VOICE COMMANDS(2017)
• → Locating Disruptions on Internet Paths through End-to-End Measurements(2012)
• → Module 5: End Use Assessment - Lesson 5.1: Introduction to End Use Assessment [Slides](2022)