标题： 阿拉伯情感网：一种用于鲁棒阿拉伯语语音情感识别的轻量级混合2D CNN-BiLSTM模型与注意力机制 显示英文标题

标题： ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition

摘要： 语音情感识别对于人机交互至关重要，特别是对于阿拉伯语等资源匮乏的语言，由于数据和研究有限而面临挑战。 我们引入了ArabEmoNet，一种轻量级架构，旨在克服这些限制并实现最先进的性能。 与之前依赖离散MFCC特征和1D卷积的系统不同，这些系统会丢失细微的频谱时间模式， ArabEmoNet使用通过2D卷积处理的梅尔频谱图，保留了传统方法中常丢失的关键情感线索。 尽管最近的模型倾向于使用数百万参数的大规模架构，但ArabEmoNet仅用100万参数就实现了更优的结果，比HuBERT基础版小90倍，比Whisper小74倍。 这种效率使其非常适合资源受限的环境。 ArabEmoNet推动了阿拉伯语语音情感识别的发展，为实际应用提供了卓越的性能和可访问性。 显示英文摘要

摘要： Speech emotion recognition is vital for human-computer interaction, particularly for low-resource languages like Arabic, which face challenges due to limited data and research. We introduce ArabEmoNet, a lightweight architecture designed to overcome these limitations and deliver state-of-the-art performance. Unlike previous systems relying on discrete MFCC features and 1D convolutions, which miss nuanced spectro-temporal patterns, ArabEmoNet uses Mel spectrograms processed through 2D convolutions, preserving critical emotional cues often lost in traditional methods. While recent models favor large-scale architectures with millions of parameters, ArabEmoNet achieves superior results with just 1 million parameters, 90 times smaller than HuBERT base and 74 times smaller than Whisper. This efficiency makes it ideal for resource-constrained environments. ArabEmoNet advances Arabic speech emotion recognition, offering exceptional performance and accessibility for real-world applications.