Sequential Editing for Lifelong Training of Speech Recognition Models

TL;DR

This paper introduces Sequential Model Editing, a new lifelong learning method for speech recognition that improves performance without needing prior data or extra parameters, achieving significant error reduction.

Contribution

The proposed Sequential Model Editing method enables continual learning in ASR systems without access to previous data or additional parameters, unlike existing techniques.

Findings

Achieves up to 15% WERR over fine-tuning baseline

Demonstrates superior efficiency over other lifelong learning methods

Effective on CommonVoice English multi-accent dataset

Abstract

Automatic Speech Recognition (ASR) traditionally assumes known domains, but adding data from a new domain raises concerns about computational inefficiencies linked to retraining models on both existing and new domains. Fine-tuning solely on new domain risks Catastrophic Forgetting (CF). To address this, Lifelong Learning (LLL) algorithms have been proposed for ASR. Prior research has explored techniques such as Elastic Weight Consolidation, Knowledge Distillation, and Replay, all of which necessitate either additional parameters or access to prior domain data. We propose Sequential Model Editing as a novel method to continually learn new domains in ASR systems. Different than previous methods, our approach does not necessitate access to prior datasets or the introduction of extra parameters. Our study demonstrates up to 15% Word Error Rate Reduction (WERR) over fine-tuning baseline, and superior efficiency over other LLL techniques on CommonVoice English multi-accent dataset.