2024
DOI: 10.1007/s11063-024-11661-6
|View full text |Cite
|
Sign up to set email alerts
|

A Lightweight Task-Agreement Meta Learning for Low-Resource Speech Recognition

Yaqi Chen,
Hao Zhang,
Wenlin Zhang
et al.

Abstract: Meta-learning has proven to be a powerful paradigm for transferring knowledge from prior tasks to facilitate the quick learning of new tasks in automatic speech recognition. However, the differences between languages (tasks) lead to variations in task learning directions, causing the harmful competition for model’s limited resources. To address this challenge, we introduce the task-agreement multilingual meta-learning (TAMML), which adopts the gradient agreement algorithm to guide the model parameters towards … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 16 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?