  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.
