DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation

TL;DR

DITTO is a novel data selection method that efficiently identifies representative speech samples for accent-specific ASR fine-tuning, supporting multiple accents and outperforming existing methods in label efficiency.

Contribution

It introduces a submodular mutual information-based approach for targeted subset selection that supports multi-accent fairness in ASR adaptation.

Findings

DITTO achieves 3-5x higher label efficiency than existing methods.

Supports fair selection across multiple accents.

Effective on IndicTTS and L2 datasets.

Abstract

State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent, a commonly adopted solution is to finetune the ASR model using accent-specific labeled speech. However, acquiring large amounts of labeled speech for specific target accents is challenging. Choosing an informative subset of speech samples that are most representative of the target accents becomes important for effective ASR finetuning. To address this problem, we propose DITTO (Data-efficient and faIr Targeted subseT selectiOn) that uses Submodular Mutual Information (SMI) functions as acquisition functions to find the most informative set of utterances matching a target accent within a fixed budget. An important feature of DITTO is that it supports fair targeting for multiple accents, i.e. it can automatically select representative data points from multiple accents when the ASR model needs to perform well on more than one accent. We show that DITTO is 3-5 times more label-efficient than other speech selection methods on the IndicTTS and L2 datasets.