Speech Corpora Divergence Based Unsupervised Data Selection for ASR

TL;DR

This paper introduces an unsupervised speech corpora divergence method for selecting training data that closely matches target speech characteristics, improving ASR performance without requiring labeled data.

Contribution

It proposes a novel unsupervised data selection approach based on speech corpora divergence using self-supervised models, enhancing diversity and acoustic detail focus.

Findings

Achieves 14.8% relative improvement over random selection

Performs comparably or better than supervised selection methods

Effective across different accents in Common Voice dataset

Abstract

Selecting application scenarios matching data is important for the automatic speech recognition (ASR) training, but it is difficult to measure the matching degree of the training corpus. This study proposes a unsupervised target-aware data selection method based on speech corpora divergence (SCD), which can measure the similarity between two speech corpora. We first use the self-supervised Hubert model to discretize the speech corpora into label sequence and calculate the N-gram probability distribution. Then we calculate the Kullback-Leibler divergence between the N-grams as the SCD. Finally, we can choose the subset which has minimum SCD to the target corpus for annotation and training. Compared to previous data selection method, the SCD data selection method can focus on more acoustic details and guarantee the diversity of the selected set. We evaluate our method on different accents from Common Voice. Experiments show that the proposed SCD data selection can realize 14.8% relative improvements to the random selection, comparable or even superior to the result of supervised selection.