Speech-to-text services aim to transcribe input audio as accurately as
possible. They increasingly play a role in everyday life, for example in
personal voice assistants or in customer-company interactions. We evaluate Open
AI's Whisper, a state-of-the-art service outperforming industry competitors.
While many of Whisper's transcriptions were highly accurate, we found that
roughly 1% of audio transcriptions contained entire hallucinated phrases or
sentences, which did not exist in any form in the underlying audio. We
thematically analyze the Whisper-hallucinated content, finding that 38% of
hallucinations include explicit harms such as violence, made up personal
information, or false video-based authority. We further provide hypotheses on
why hallucinations occur, uncovering potential disparities due to speech type
by health status. We call on industry practitioners to ameliorate these
language-model-based hallucinations in Whisper, and to raise awareness of
potential biases in downstream applications of speech-to-text models.

评估了 OpenAI 的 Whisper 语音转文字服务，发现大约 1% 的音频转录中包含了并不存在于音频中的幻觉短语或句子，其中 38% 的幻觉内容包括暴力、虚构的个人信息或虚假的基于视频的权威，并提供了关于幻觉发生原因的假设，揭示了由语音类型和健康状况引起的潜在差异。呼吁业界从业者改善 Whisper 中基于语言模型的幻觉问题，并提高对语音转文字模型在下游应用中潜在偏见的意识。