基于高效通道注意力引导的深度CNN-BiLSTM框架的增强型语音情感识别
Enhanced Speech Emotion Recognition with Efficient Channel Attention Guided Deep CNN-BiLSTM Framework
AI总结：
针对语音情感识别中低成本选取相关特征的难题，提出融合注意力局部特征块与全局特征块的轻量级CNN-BiLSTM框架，在五个多语言基准数据集上取得当前最优的识别准确率。
摘要：
语音情感识别（SER）对增强情感计算、丰富人机交互领域至关重要。然而，SER的主要挑战在于以更低的计算成本从语音信号中选择相关的特征表示。本文提出了一种轻量级SER架构，该架构整合了基于注意力的局部特征块（ALFB），用于从语音信号中捕获高层相关特征向量。我们还引入了全局特征块（GFB）技术，以捕获语音信号中的序列全局信息和长期依赖关系。通过聚合基于注意力的局部和全局上下文特征向量，我们的模型能够有效捕获反映人类复杂情感线索的显著特征之间的内在关联。为评估所提方法，我们从语音音频样本中提取了四类频谱特征：梅尔频率倒谱系数、梅尔频谱图、均方根值和过零率。通过5折交叉验证策略，我们在五个多语言标准基准数据集（TESS、RAVDESS、BanglaSER、SUBESCO和Emo-DB）上测试了所提方法，分别获得了99.65%、94.88%、98.12%、97.94%和97.19%的平均准确率。结果表明，与大多数现有方法相比，我们的模型达到了SOTA（state-of-the-art，当前最优）性能。
英文摘要：
Speech emotion recognition (SER) is crucial for enhancing affective computing and enriching the domain of human-computer interaction. However, the main challenge in SER lies in selecting relevant feature representations from speech signals with lower computational costs. In this paper, we propose a lightweight SER architecture that integrates attention-based local feature blocks (ALFBs) to capture high-level relevant feature vectors from speech signals. We also incorporate a global feature block (GFB) technique to capture sequential, global information and long-term dependencies in speech signals. By aggregating attention-based local and global contextual feature vectors, our model effectively captures the internal correlation between salient features that reflect complex human emotional cues. To evaluate our approach, we extracted four types of spectral features from speech audio samples: mel-frequency cepstral coefficients, mel-spectrogram, root mean square value, and zero-crossing rate. Through a 5-fold cross-validation strategy, we tested the proposed method on five multi-lingual standard benchmark datasets: TESS, RAVDESS, BanglaSER, SUBESCO, and Emo-DB, and obtained a mean accuracy of 99.65%, 94.88%, 98.12%, 97.94%, and 97.19% respectively. The results indicate that our model achieves state-of-the-art (SOTA) performance compared to most existing methods.
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。
