Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for CREMA-D, and a perfect 100% on both TESS and EMO-DB. For the combined (R+T+S) datasets, it achieves 98.82% accuracy, outperforming previously reported results. To our knowledge, no existing study has evaluated a single SER model across all five benchmark datasets (i.e., R+T+S+C+E) simultaneously. In our work, we introduce this comprehensive combination and achieve a remarkable overall accuracy of 93.76%. These results confirm the robustness and generalizability of our DCRF-BiLSTM framework across diverse datasets.

翻译：当前，语音情感识别（SER）在人机交互（HCI）与人工智能（AI）发展中扮演着至关重要的角色。我们提出的DCRF-BiLSTM模型用于识别七种情感状态：中性、快乐、悲伤、愤怒、恐惧、厌恶和惊讶，该模型在五个数据集上进行训练：RAVDESS（R）、TESS（T）、SAVEE（S）、EmoDB（E）和Crema-D（C）。该模型在单一数据集上取得了高准确率，包括RAVDESS的97.83%、SAVEE的97.02%、CREMA-D的95.10%，以及在TESS和EMO-DB上均达到100%的完美准确率。在组合数据集（R+T+S）上，模型取得了98.82%的准确率，超越了先前报道的结果。据我们所知，现有研究尚未有在全部五个基准数据集（即R+T+S+C+E）上同时评估单一SER模型的工作。本研究首次引入这种全面组合，并取得了93.76%的显著总体准确率。这些结果证实了我们的DCRF-BiLSTM框架在不同数据集间具有优异的鲁棒性与泛化能力。