Sequential Editing for Lifelong Training of Speech Recognition Models

TL;DR

This work proposes Sequential Model Editing as a novel method to continually learn new domains in ASR systems that does not necessitate access to prior datasets or the introduction of extra parameters.

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.

Figures (3)

• Figure 1: Sequential Model Editing for Lifelong ASR: At each time step $t$, the current model $\theta_{t-1}$ is fine-tuned on data $\mathcal{D}_t$ to obtain $\hat{\theta_t}$. Then task vector $\tau_t$ is computed. Finally, new model is obtained my merging $\tau_t$ with $\theta_{t-1}$ as : $\theta_t = \theta_{t-1} + \lambda \cdot (\tau_t)$.
• Figure 2: Evolution of WER on seen accents for various approaches as new accents are added incrementally.
• Figure 3: Variation in WER for previous seen accents vs new accent for $t=2$ for different $\lambda$.