这篇论文拥有最聪明的审稿人——利用基于音频-文本Transformer的方法进行奉承检测
This Paper Had the Smartest Reviewers -- Flattery Detection Utilising an Audio-Textual Transformer-Based Approach
AI总结：
本研究构建了一个20小时的音频文本数据集，采用AST、Wav2Vec2、Whisper和RoBERTa等模型，提出了一种基于Transformer的多模态奉承检测方法，在未见数据上取得了高达87.16%的未加权平均召回率。
摘要：
奉承是人类沟通的重要方面，通过策略性的赞美和表扬促进社交联系、塑造认知并影响行为，利用言语的力量有效建立融洽关系。因此，自动检测奉承可以增强人机交互的自然性。为满足这一需求，我们提出了一个包含20小时语音的新型音频文本数据集，并训练机器学习模型进行自动奉承检测。具体而言，我们在语音模态中采用预训练的AST、Wav2Vec2和Whisper模型，在文本模态中采用Whisper TTS模型结合RoBERTa文本分类器。随后，我们通过结合文本和音频表示构建了多模态分类器。在未见过的测试数据上的评估显示出有前景的结果，Unweighted Average Recall得分在仅音频实验中达到82.46%，在仅文本实验中达到85.97%，在使用多模态方法时达到87.16%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
