  Flattery is an important aspect of human communication that facilitates
social bonding, shapes perceptions, and influences behavior through strategic
compliments and praise, leveraging the power of speech to build rapport
effectively. Its automatic detection can thus enhance the naturalness of
human-AI interactions. To meet this need, we present a novel audio textual
dataset comprising 20 hours of speech and train machine learning models for
automatic flattery detection. In particular, we employ pretrained AST,
Wav2Vec2, and Whisper models for the speech modality, and Whisper TTS models
combined with a RoBERTa text classifier for the textual modality. Subsequently,
we build a multimodal classifier by combining text and audio representations.
Evaluation on unseen test data demonstrates promising results, with Unweighted
Average Recall scores reaching 82.46% in audio-only experiments, 85.97% in
text-only experiments, and 87.16% using a multimodal approach.
