阶梯网络用于半监督语音情感识别
Semi-Supervised Speech Emotion Recognition with Ladder Networks
AI总结：
本研究提出利用阶梯网络进行半监督语音情感识别，通过无监督辅助任务提升模型泛化能力，在语料库内和跨语料库评估中均优于单任务和多任务学习基线。
摘要：
语音情感识别（SER）系统在医疗保健、教育以及安全和国防等多个领域都有应用。这些系统的一个主要缺点是它们在不同条件下的泛化能力不足。这个问题可以通过在目标领域的大量标记数据上训练模型来解决，但这既昂贵又耗时。另一种方法是提高模型的泛化能力。实现这一目标的有效方法是通过多任务学习（MTL）对模型进行正则化，即在主任务之外学习辅助任务。这些方法通常需要使用标记数据，而收集这些数据对于情感识别（性别、说话人身份、年龄或其他情感描述符）来说计算成本高昂。本研究提出使用阶梯网络进行情感识别，该网络利用了一个无监督的辅助任务。主任务是一个预测情感属性的回归问题。辅助任务是使用去噪自编码器重建中间特征表示。该辅助任务不需要标签，因此可以在半监督的方式下，利用目标领域的大量未标记数据进行框架训练。研究表明，所提出的方法为SER创建了一个强大的框架，其性能优于完全监督的单任务学习（STL）和多任务学习（MTL）基线。该方法使用多种声学特征实现，表明阶梯网络在跨语料库设置中具有显著更好的泛化能力。与STL基线相比，所提出的方法在语料库内评估中，一致性相关系数（CCC）的相对增益在3.0%到3.5%之间，在跨语料库评估中，相对增益在16.1%到74.1%之间，突显了该架构的强大能力。
英文摘要：
Speech emotion recognition (SER) systems find applications in various fields such as healthcare, education, and security and defense. A major drawback of these systems is their lack of generalization across different conditions. This problem can be solved by training models on large amounts of labeled data from the target domain, which is expensive and time-consuming. Another approach is to increase the generalization of the models. An effective way to achieve this goal is by regularizing the models through multitask learning (MTL), where auxiliary tasks are learned along with the primary task. These methods often require the use of labeled data which is computationally expensive to collect for emotion recognition (gender, speaker identity, age or other emotional descriptors). This study proposes the use of ladder networks for emotion recognition, which utilizes an unsupervised auxiliary task. The primary task is a regression problem to predict emotional attributes. The auxiliary task is the reconstruction of intermediate feature representations using a denoising autoencoder. This auxiliary task does not require labels so it is possible to train the framework in a semi-supervised fashion with abundant unlabeled data from the target domain. This study shows that the proposed approach creates a powerful framework for SER, achieving superior performance than fully supervised single-task learning (STL) and MTL baselines. The approach is implemented with several acoustic features, showing that ladder networks generalize significantly better in cross-corpus settings. Compared to the STL baselines, the proposed approach achieves relative gains in concordance correlation coefficient (CCC) between 3.0% and 3.5% for within corpus evaluations, and between 16.1% and 74.1% for cross corpus evaluations, highlighting the power of the architecture.
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。
