Abstract
Modern ASR systems are typically trained on large-scale pseudo-labeled, in-the-wild data spanning multiple domains. While such heterogeneous data benefit generalist models designed for broad deployment, they pose challenges for specialist models targeting specific domains: specialist models lack the capacity to learn from all available data, and one must pay closer attention to addressing the mismatch between training and test conditions. In this work, we study targeted data selection as a strategy to address these challenges, selecting relevant subsets from 100k hours of in-the-wild training data to optimize performance on target domains. We represent speech samples using embeddings that capture complementary characteristic--speaker attributes, phonetic content, and semantic meaning--and analyze how relevance and diversity along these axes when performing data selection affect downstream ASR performance. Our experiments with CTC-based Conformer models show that training on a strategically selected 5% subset can exceed the performance of models trained on the full dataset by up to 36.8% relative WER reduction on target domains.
Abstract (translated)
现代的自动语音识别（ASR）系统通常是在大规模伪标注的真实场景数据上进行训练，这些数据涵盖了多个领域。虽然这种异质性数据对旨在广泛部署的一般模型有利，但对于针对特定领域的专业模型来说却带来了挑战：专业模型缺乏从所有可用数据中学习的能力，并且必须更加注意解决训练和测试条件之间的不匹配问题。在这项工作中，我们研究了有目标的数据选择策略来应对这些挑战，即从10万小时的真实场景训练数据中挑选出相关的子集以优化特定领域的性能。我们将语音样本表示为捕获互补特征的嵌入向量——包括说话人属性、音素内容和语义意义，并分析在进行数据选择时沿这些轴的相关性和多样性如何影响下游ASR性能。我们的实验表明，基于CTC的Conformer模型在经过精心挑选的5%子集上训练时，在目标领域中的相对错误词率（WER）可以比在完整数据集上训练的模型降低高达36.8%，这证明了有目标的数据选择策略的有效性。
URL
https://arxiv.org/abs/2603.05819
