2023/06/19 by Steven Vander Eeckt, Eeckt, Steven Vander, Hugo Van hamme +1 · 2 citations
Computer Science · #Audio and Speech Processing (eess.AS) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #FOS: Electrical engineering #Multimodal Machine Learning Applications #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2306.10860
openalex publication_date 2023/06/19 · openalex created_date 2023/06/22 · openalex updated_date 2026/07/28
Fine-tuning an Automatic Speech Recognition (ASR) model to new domains results in degradation on original domains, referred to as Catastrophic Forgetting (CF). Continual Learning (CL) attempts to train ASR models without suffering from CF. While in ASR, offline CL is usually considered, online CL is a more realistic but also more challenging scenario where the model, unlike in offline CL, does not know when a task boundary occurs. Rehearsal-based methods, which store previously seen utterances in a memory, are often considered for online CL, in ASR and other research domains. However, recent research has shown that weight averaging is an effective method for offline CL in ASR. Based on this result, we propose, in this paper, a rehearsal-free method applicable for online CL. Our method outperforms all baselines, including rehearsal-based methods, in two experiments. Our method is a next step towards general CL for ASR, which should enable CL in all scenarios with few if any constraints.