Five Calls Can Change the Model Choice
TL;DR for operators A fraud team launching a voice-phishing detector faces a sequencing decision before it faces a model-selection decision. In this benchmark, a four-feature acoustic representation reaches 69.5% F1 when no authentic voice-phishing examples are added to classifier training, beating frozen HuBERT at 58.3% and wav2vec2.0 at 36.2%. But the ranking changes after only five authentic fraud examples: HuBERT rises to 94.2% F1, wav2vec2.0 to 90.2%, while the four-feature system reaches 71.0%.1 ...