通过自监督学习特征的分段平均池化增强语音情感识别
Enhancing Speech Emotion Recognition through Segmental Average Pooling of Self-Supervised Learning Features
AI总结：
本文提出分段平均池化（SAP）方法，通过选择性聚焦语音段并忽略非语音段，结合全局平均池化（GAP）提升自监督学习特征在语音情感识别中的性能，在IEMOCAP和KEMDy19数据集上取得最优结果。
摘要：
语音情感识别（SER）分析通过语音表达的人类情感。自监督学习（SSL）通过从大量无标签音频数据中学习有意义的表示，为SER提供了一种有前景的方法。然而，现有的基于SSL的方法依赖全局平均池化（GAP）来表示音频信号，将语音和非语音段同等对待。这可能导致信息性语音特征被无关的非语音信息稀释。为解决这一问题，本文提出分段平均池化（SAP），选择性地聚焦于信息性语音段而忽略非语音段。通过将GAP和SAP同时应用于SSL特征，我们的方法利用GAP提供的整体语音信号信息和SAP提供的特定信息，从而提升SER性能。实验表明，在英语数据集IEMOCAP和韩语数据集KEMDy19上，该方法在未加权和加权准确率方面均取得了当前最优结果。
英文摘要：
Speech Emotion Recognition (SER) analyzes human emotions expressed through speech. Self-supervised learning (SSL) offers a promising approach to SER by learning meaningful representations from a large amount of unlabeled audio data. However, existing SSL-based methods rely on Global Average Pooling (GAP) to represent audio signals, treating speech and non-speech segments equally. This can lead to dilution of informative speech features by irrelevant non-speech information. To address this, the paper proposes Segmental Average Pooling (SAP), which selectively focuses on informative speech segments while ignoring non-speech segments. By applying both GAP and SAP to SSL features, our approach utilizes overall speech signal information from GAP and specific information from SAP, leading to improved SER performance. Experiments show state-of-the-art results on the IEMOCAP for English and superior performance on KEMDy19 for Korean datasets in both unweighted and weighted accuracies.
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。
