标题： 通过频谱学习和注意力实现高效的语音情感识别 显示英文标题

标题： Toward Efficient Speech Emotion Recognition via Spectral Learning and Attention

摘要： 语音情感识别（SER）传统上依赖于听觉数据分析来进行情感分类。 一些研究采用了不同的方法进行SER。 然而，现有的SER方法往往难以捕捉细微的情感变化，并在不同数据集之间泛化。 在本文中，我们使用梅尔频率倒谱系数（MFCCs）作为频谱特征，以弥合计算情感处理与人类听觉感知之间的差距。 为了进一步提高鲁棒性和特征多样性，我们提出了一种基于1D-CNN的新型SER框架，该框架集成了数据增强技术。 从增强数据中提取的MFCC特征通过一个结合了通道和空间注意力机制的1D卷积神经网络（CNN）架构进行处理。 这些注意力模块使模型能够突出关键的情感模式，增强其捕捉语音信号中细微变化的能力。 所提出的方法实现了最先进的性能，在SAVEE上的准确率为97.49%，在RAVDESS上的准确率为99.23%，在CREMA-D上的准确率为89.31%，在TESS上的准确率为99.82%，在EMO-DB上的准确率为99.53%，在EMOVO上的准确率为96.39%。 实验结果展示了SER的新基准，证明了我们的方法在高精度识别情感表达方面的有效性。 我们的评估表明，先进深度学习（DL）方法的集成显著提高了在不同数据集上的泛化能力，突显了它们在辅助技术和人机交互中的实际部署中推进SER的潜力。 显示英文摘要

摘要： Speech Emotion Recognition (SER) traditionally relies on auditory data analysis for emotion classification. Several studies have adopted different methods for SER. However, existing SER methods often struggle to capture subtle emotional variations and generalize across diverse datasets. In this article, we use Mel-Frequency Cepstral Coefficients (MFCCs) as spectral features to bridge the gap between computational emotion processing and human auditory perception. To further improve robustness and feature diversity, we propose a novel 1D-CNN-based SER framework that integrates data augmentation techniques. MFCC features extracted from the augmented data are processed using a 1D Convolutional Neural Network (CNN) architecture enhanced with channel and spatial attention mechanisms. These attention modules allow the model to highlight key emotional patterns, enhancing its ability to capture subtle variations in speech signals. The proposed method delivers cutting-edge performance, achieving the accuracy of 97.49% for SAVEE, 99.23% for RAVDESS, 89.31% for CREMA-D, 99.82% for TESS, 99.53% for EMO-DB, and 96.39% for EMOVO. Experimental results show new benchmarks in SER, demonstrating the effectiveness of our approach in recognizing emotional expressions with high precision. Our evaluation demonstrates that the integration of advanced Deep Learning (DL) methods substantially enhances generalization across diverse datasets, underscoring their potential to advance SER for real-world deployment in assistive technologies and human-computer interaction.