A meta learning scheme for fast accent domain expansion in Mandarin speech recognition

TL;DR

This paper introduces a meta-learning approach for rapid accent domain expansion in Mandarin speech recognition, significantly improving performance and training efficiency without compromising baseline accuracy.

Contribution

It combines meta-learning with parameter freezing to enhance accent domain adaptation in Mandarin ASR, achieving faster training and better accuracy.

Findings

Outperforms other methods by 3% in accent domain expansion

Improves baseline performance by 37% on Mandarin test set

Reduces training time by approximately 20%

Abstract

Spoken languages show significant variation across mandarin and accent. Despite the high performance of mandarin automatic speech recognition (ASR), accent ASR is still a challenge task. In this paper, we introduce meta-learning techniques for fast accent domain expansion in mandarin speech recognition, which expands the field of accents without deteriorating the performance of mandarin ASR. Meta-learning or learn-to-learn can learn general relation in multi domains not only for over-fitting a specific domain. So we select meta-learning in the domain expansion task. This more essential learning will cause improved performance on accent domain extension tasks. We combine the methods of meta learning and freeze of model parameters, which makes the recognition performance more stable in different cases and the training faster about 20%. Our approach significantly outperforms other methods about 3% relatively in the accent domain expansion task. Compared to the baseline model, it improves relatively 37% under the condition that the mandarin test set remains unchanged. In addition, it also proved this method to be effective on a large amount of data with a relative performance improvement of 4% on the accent test set.