Say again? The limits of Whisper with conversation: A case study on the KIParla corpus

This study investigates how Whisper handles interactional phenomena in spontaneous Italian conversation, focusing on backchannels, repairs, and filled pauses. We compare standard Word Error Rate (WER) optimization with a decoding strategy that explicitly rewards the preservation of interactional events. Results show that decoding choices have limited impact on overall accuracy, while recognition remains strongly phenomenon-dependent, suggesting structural limitations in the handling of interactional phenomena, with systematic linearization of repairs and frequent suppression of short conversational items.