Abstract
Speech emotion recognition is a challenging task in speech processing field. For this reason, feature extraction process has a crucial importance to demonstrate and process the speech signals. In this work, we represent a model, which feeds raw audio files directly into the deep neural networks without any feature extraction stage for the recognition of emotions utilizing six different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. To demonstrate the contribution of proposed model, the performance of traditional feature extraction techniques namely, mel-scale spectogram, mel-frequency cepstral coefficients, are blended with machine learning algorithms, ensemble learning methods, deep and hybrid deep learning techniques. Support vector machine, decision tree, naive Bayes, random forests models are evaluated as machine learning algorithms while majority voting and stacking methods are assessed as ensemble learning techniques. Moreover, convolutional neural networks, long short-term memory networks, and hybrid CNN- LSTM model are evaluated as deep learning techniques and compared with machine learning and ensemble learning methods. To demonstrate the effectiveness of proposed model, the comparison with state-of-the-art studies are carried out. Based on the experiment results, CNN model excels existent approaches with 95.86% of accuracy for TESS+RAVDESS data set using raw audio files, thence determining the new state-of-the-art. The proposed model performs 90.34% of accuracy for EMO-DB with CNN model, 90.42% of accuracy for RAVDESS with CNN model, 99.48% of accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model, 85.76% of accuracy for SAVEE with CNN model in speaker-independent audio categorization problems.
Abstract (translated)
语音识别在语音处理领域是一项具有挑战性的任务。因此,特征提取过程对于展示和处理语音信号至关重要。在本研究中,我们代表了一个模型,该模型将 raw audio files直接输入深度神经网络,而不需要特征提取阶段,以识别情感利用六个不同的数据集,EMO-DB、RAVDESS、TESS、CREMA、SaveE和TESS+RAVDESS。为了证明该模型的贡献,传统的特征提取技术的性能,例如 Mel 尺度spectogram、Mel 频率cepstral coefficients 被与机器学习算法、群体学习方法、深度和混合深度学习技术Blended 一起评估。支持向量机、决策树、Naive Bayes、随机森林模型被评为机器学习算法,群体投票和堆叠方法被评为群体学习方法。此外,卷积神经网络、长期短期记忆网络和混合 CNN-LSTM 模型被评为深度学习技术,并与机器学习和群体学习方法进行比较。为了证明该模型的有效性,进行了与现有方法的比较。根据实验结果,CNN 模型在 TESS+RAVDESS 数据集上表现优异,使用 raw audio files 时准确率为 95.86%,因此确定了新的先进技术。该模型在 EMO-DB 与 CNN 模型使用时准确率为 90.34%,在 RAVDESS 与 CNN 模型使用时准确率为 90.42%,在 TESS 与 LSTM 模型使用时准确率为 99.48%,在 CREMA 与 CNN 模型使用时准确率为 69.72%,在 SaveE 与 CNN 模型使用时准确率为 85.76% 解决 speaker-independent 音频分类问题。
URL
https://arxiv.org/abs/2307.02820
