用零样本语音情绪识别直接估计购买意愿，效果媲美有监督模型。
Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?
- 基于对比语言音频预训练框架，支持多任务多类别零样本情绪识别。
- 在未知双极情绪上表现优异，零样本估计效果接近有监督模型。
- 首次实现从语音直接预测购买意愿，适合电商和用户行为研究者。
本文提出一种零样本语音情绪识别（SER）方法，可识别未在模型训练中定义的情绪。传统方法仅限于单个词语定义的情绪识别，而本研究旨在识别如“我想买 - 我不想买”这类未知的双极情绪。为此，方法在对比语言-音频预训练（CLAP）框架基础上，引入多类别与多任务设置，使模型能自由使用句子定义类别，并评估未知双极情绪。研究聚焦购买意愿这一双极情绪，探究模型在零样本条件下的表现。这是首次直接从语音中估计购买意愿的研究。实验表明，该方法的零样本估计结果达到与有监督学习模型相当的水平。
原文摘要 · Abstract (English)
This paper proposes a zero-shot speech emotion recognition (SER) method that estimates emotions not previously defined in the SER model training. Conventional methods are limited to recognizing emotions defined by a single word. Moreover, we have the motivation to recognize unknown bipolar emotions such as ``I want to buy - I do not want to buy.'' In order to allow the model to define classes using sentences freely and to estimate unknown bipolar emotions, our proposed method expands upon the contrastive language-audio pre-training (CLAP) framework by introducing multi-class and multi-task settings. We also focus on purchase intention as a bipolar emotion and investigate the model's performance to zero-shot estimate it. This study is the first attempt to estimate purchase intention from speech directly. Experiments confirm that the results of zero-shot estimation by the proposed method are at the same level as those of the model trained by supervised learning.
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