The challenges facing speech recognition systems, such as variations in pronunciations, adverse audio conditions, and the scarcity of labeled data, emphasize the necessity for a post-processing step that corrects recurring errors. Previous research has shown the advantages of employing dedicated error correction models, yet training such models requires large amounts of labeled data which is not easily obtained. To overcome this limitation, synthetic transcribed-like data is often utilized, however, bridging the distribution gap between transcribed errors and synthetic noise is not trivial. In this paper, we demonstrate that the performance of correction models can be significantly increased by training solely using synthetic data. Specifically, we empirically show that: (1) synthetic data generated using the error distribution derived from a set of transcribed data outperforms the common approach of applying random perturbations; (2) applying language-specific adjustments to the vocabulary of a BPE tokenizer strike a balance between adapting to unseen distributions and retaining knowledge of transcribed errors. We showcase the benefits of these key observations, and evaluate our approach using multiple languages, speech recognition systems and prominent speech recognition datasets.

翻译：语音识别系统面临的挑战，例如发音变异性、不利的音频环境以及标注数据的稀缺性，凸显了进行后处理以校正系统性能的关键需求。先前研究已展示了采用专用错误校正模型的优势，然而训练此类模型需要大量难以获取的标注数据。为克服这一限制，通常使用合成转录类数据，但弥合转录错误与合成噪声之间的分布差异并非易事。本文证明，仅通过合成数据训练即可显著提升校正模型的性能。具体而言，我们通过实验表明：（1）基于从转录数据中提取的错误分布生成合成数据，其效果优于常见的随机扰动方法；（2）对BPE分词器的词汇表进行语言特定调整，可在适应未见分布与保留转录错误知识之间取得平衡。我们展示了这些关键发现的优势，并通过多种语言、语音识别系统及主流语音识别数据集评估了所提方法。