2022/03/30 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: Electrical engineering #Multimodal Machine Learning Applications #Topic Modeling #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2203.16082
openalex publication_date 2022/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Learning a set of tasks in sequence remains a challenge for artificial neural networks, which, in such scenarios, tend to suffer from Catastrophic Forgetting (CF). The same applies to End-to-End (E2E) Automatic Speech Recognition (ASR) models, even for monolingual tasks. In this paper, we aim to overcome CF for E2E ASR by inserting adapters, small architectures of few parameters which allow a general model to be fine-tuned to a specific task, into our model. We make these adapters task-specific, while regularizing the parameters of the model shared by all tasks, thus stimulating the model to fully exploit the adapters while keeping the shared parameters to work well for all tasks. Our method outperforms all baselines on two monolingual experiments while being more storage efficient and without requiring the storage of data from previous tasks.