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

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 a direction where tasks exhibit greater consistency. However, the computation and storage cost of TAMML grows dramatically with model’s depth increases. To address this, we further propose a simplification called TAMML-Light which only uses the output layer for gradient calculation. Experiments on three datasets demonstrate that TAMML and TAMML-Light achieve outperform meta-learning approaches, yielding superior results.Furthermore, TAMML-Light can reduce at least 80 \(\%\) $\%$ of the relative increased computation expenses compared to TAMML.