EmoFormer：一种基于混合Transformer-CNN模型的文本无关语音情感识别
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model
AI总结：
本研究提出EmoFormer混合模型，结合CNN与Transformer，在EARS数据集上实现文本无关语音情感识别，五类情感时准确率达90%，优于基线网络。
摘要：
语音情感识别是人机交互中的一个关键研究领域。尽管该领域已有大量工作，但许多最先进的网络在数据既与语音无关又与说话人无关时，难以准确识别语音中的情感。为解决这一局限，本研究提出了EmoFormer，一种将CNN与Transformer编码器相结合的混合模型，以捕获此类独立数据集中的语音情感模式。EmoFormer网络使用META最近发布的表达性无回声语音录音（EARS）数据集进行训练和测试。我们实验了两种特征提取技术：MFCC和x-vector。该模型在包含5、7、10和23个不同类别的情感集上进行了评估。结果表明，该模型在五种情感上取得了最佳性能，准确率达到90%，精确率为0.92，召回率和F1分数均为0.91。然而，随着情感数量的增加，性能有所下降，七种情感的准确率为83%，而基线网络为70%。本研究强调了将CNN和基于Transformer的架构相结合用于语音情感识别的有效性，尤其是在使用MFCC特征时。
英文摘要：
Speech Emotion Recognition is a crucial area of research in human-computer interaction. While significant work has been done in this field, many state-of-the-art networks struggle to accurately recognize emotions in speech when the data is both speech and speaker-independent. To address this limitation, this study proposes, EmoFormer, a hybrid model combining CNNs (CNNs) with Transformer encoders to capture emotion patterns in speech data for such independent datasets. The EmoFormer network was trained and tested using the Expressive Anechoic Recordings of Speech (EARS) dataset, recently released by META. We experimented with two feature extraction techniques: MFCCs and x-vectors. The model was evaluated on different emotion sets comprising 5, 7, 10, and 23 distinct categories. The results demonstrate that the model achieved its best performance with five emotions, attaining an accuracy of 90%, a precision of 0.92, a recall, and an F1-score of 0.91. However, performance decreased as the number of emotions increased, with an accuracy of 83% for seven emotions compared to 70% for the baseline network. This study highlights the effectiveness of combining CNNs and Transformer-based architectures for emotion recognition from speech, particularly when using MFCC features.
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。
