The rapid advancements in speech technologies over the past two decades have
led to human-level performance in tasks like automatic speech recognition (ASR)
for fluent speech. However, the efficacy of these models diminishes when
applied to atypical speech, such as stuttering. This paper introduces AS-70,
the first publicly available Mandarin stuttered speech dataset, which stands
out as the largest dataset in its category. Encompassing conversational and
voice command reading speech, AS-70 includes verbatim manual transcription,
rendering it suitable for various speech-related tasks. Furthermore, baseline
systems are established, and experimental results are presented for ASR and
stuttering event detection (SED) tasks. By incorporating this dataset into the
model fine-tuning, significant improvements in the state-of-the-art ASR models,
e.g., Whisper and Hubert, are observed, enhancing their inclusivity in
addressing stuttered speech.

通过引入 AS-70，首个公开可用的普通话口吃语音数据集，本研究旨在改善自动语音识别（ASR）模型在口吃语音上表现的效果。该数据集包括对话和语音命令朗读内容的逐字手工转录，适用于多种与语音相关的任务。实验结果表明，通过将此数据集应用于模型微调，可以显著改善现有 ASR 模型的性能，提高了对口吃语音的包容性。