2,172 research outputs found
Damage Control During Domain Adaptation for Transducer Based Automatic Speech Recognition
Automatic speech recognition models are often adapted to improve their
accuracy in a new domain. A potential drawback of model adaptation to new
domains is catastrophic forgetting, where the Word Error Rate on the original
domain is significantly degraded. This paper addresses the situation when we
want to simultaneously adapt automatic speech recognition models to a new
domain and limit the degradation of accuracy on the original domain without
access to the original training dataset. We propose several techniques such as
a limited training strategy and regularized adapter modules for the Transducer
encoder, prediction, and joiner network. We apply these methods to the Google
Speech Commands and to the UK and Ireland English Dialect speech data set and
obtain strong results on the new target domain while limiting the degradation
on the original domain.Comment: To appear in Proc. SLT 2022, Jan 09-12, 2023, Doha, Qata
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech Recognition
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.Comment: Submitted to ICASSP 2023. 5 page
Online Continual Learning of End-to-End Speech Recognition Models
Continual Learning, also known as Lifelong Learning, aims to continually
learn from new data as it becomes available. While prior research on continual
learning in automatic speech recognition has focused on the adaptation of
models across multiple different speech recognition tasks, in this paper we
propose an experimental setting for \textit{online continual learning} for
automatic speech recognition of a single task. Specifically focusing on the
case where additional training data for the same task becomes available
incrementally over time, we demonstrate the effectiveness of performing
incremental model updates to end-to-end speech recognition models with an
online Gradient Episodic Memory (GEM) method. Moreover, we show that with
online continual learning and a selective sampling strategy, we can maintain an
accuracy that is similar to retraining a model from scratch while requiring
significantly lower computation costs. We have also verified our method with
self-supervised learning (SSL) features.Comment: Accepted at InterSpeech 202
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