  Automatic text-based diacritic restoration models generally have high
diacritic error rates when applied to speech transcripts as a result of domain
and style shifts in spoken language. In this work, we explore the possibility
of improving the performance of automatic diacritic restoration when applied to
speech data by utilizing parallel spoken utterances. In particular, we use the
pre-trained Whisper ASR model fine-tuned on relatively small amounts of
diacritized Arabic speech data to produce rough diacritized transcripts for the
speech utterances, which we then use as an additional input for diacritic
restoration models. The proposed framework consistently improves diacritic
restoration performance compared to text-only baselines. Our results highlight
the inadequacy of current text-based diacritic restoration models for speech
data sets and provide a new baseline for speech-based diacritic restoration.
