The Open Whisper-style Speech Model (OWSM) series was introduced to achieve
full transparency in building advanced speech-to-text (S2T) foundation models.
To this end, OWSM models are trained on 25 public speech datasets, which are
heterogeneous in multiple ways. In this study, we advance the OWSM series by
introducing OWSM v3.2, which improves on prior models by investigating and
addressing the impacts of this data heterogeneity. Our study begins with a
detailed analysis of each dataset, from which we derive two key strategies:
data filtering with proxy task to enhance data quality, and the incorporation
of punctuation and true-casing using an open large language model (LLM). With
all other configurations staying the same, OWSM v3.2 improves performance over
the OWSM v3.1 baseline while using 15% less training data.

通过分析数据集，我们引入了 OWSM v3.2，采用两种关键策略（代理任务筛选数据和使用大型开放语言模型进行标点符号和大小写处理），从而提高了 Open Whisper-style Speech Model v3.1 基线的性能，并减少了 15% 的训练数据。