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.

本研究提出了一种基于语音语料库分歧的无监督目标感知数据选择方法，使用自监督 Hubert 模型将语音语料库离散化为标签序列，计算 N-gram 概率分布，并计算 N-gram 之间的 KL 散度作为 SCD，从而选择与目标语料库具有最小 SCD 的子集进行注释和训练。与以往的数据选择方法相比，SCD 数据选择方法可以关注更多的声学细节并保证所选集的多样性。在 Common Voice 的不同口音上进行评估，实验结果表明，所提出的 SCD 数据选择方法可以实现 14.8% 的相对改进，与有监督的选择结果相当或甚至更好。