Semi-FedSER：基于联邦学习和多视图伪标签的半监督语音情感识别
Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling
AI总结：
针对联邦学习下语音情感识别标注数据稀缺与隐私保护问题，提出半监督框架Semi-FedSER，利用多视图伪标签同时使用标注与未标注数据，在IEMOCAP和MSP-Improv上实现标签率仅20%时的良好性能。
摘要：
语音情感识别（SER）应用通常伴随隐私担忧，因为它常在客户端获取并传输语音数据到远程云平台进行进一步处理。这些语音数据不仅可能泄露语音内容和情感信息，还可能泄露说话人身份、人口统计学特征和健康状况。联邦学习（FL）是一种分布式机器学习算法，它协调客户端在不共享本地数据的情况下协作训练模型。该算法在SER应用中展现出巨大潜力，因为共享来自用户设备的原始语音或语音特征容易受到隐私攻击。然而，FL中的一个主要挑战是高质量标注数据样本的有限可用性。在本工作中，我们提出了一种半监督联邦学习框架Semi-FedSER，它同时利用标注和未标注数据样本来应对FL中标注数据样本有限的挑战。我们使用两个SER基准数据集IEMOCAP和MSP-Improv表明，即使在本地标签率l=20的情况下，我们的Semi-FedSER也能产生期望的SER性能。
英文摘要：
Speech Emotion Recognition (SER) application is frequently associated with privacy concerns as it often acquires and transmits speech data at the client-side to remote cloud platforms for further processing. These speech data can reveal not only speech content and affective information but the speaker's identity, demographic traits, and health status. Federated learning (FL) is a distributed machine learning algorithm that coordinates clients to train a model collaboratively without sharing local data. This algorithm shows enormous potential for SER applications as sharing raw speech or speech features from a user's device is vulnerable to privacy attacks. However, a major challenge in FL is limited availability of high-quality labeled data samples. In this work, we propose a semi-supervised federated learning framework, Semi-FedSER, that utilizes both labeled and unlabeled data samples to address the challenge of limited labeled data samples in FL. We show that our Semi-FedSER can generate desired SER performance even when the local label rate l=20 using two SER benchmark datasets: IEMOCAP and MSP-Improv.
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