Hybrid Data Augmentation And Deep Attention-based Dilated Convolutional-recurrent Neural Networks For Speech Emotion Recognition

Abstract

Speech emotion recognition (SER) has been one of the significant tasks in Human-Computer Interaction (HCI) applications. However, it is hard to choose the optimal features and deal with imbalance labeled data. In this article, we investigate hybrid data augmentation (HDA) methods to generate and balance data based on traditional and generative adversarial networks (GAN) methods. To evaluate the effectiveness of HDA methods, a deep learning framework namely (ADCRNN) is designed by integrating deep dilated convolutional-recurrent neural networks with an attention mechanism. Besides, we choose 3D log Mel-spectrogram (MelSpec) features as the inputs for the deep learning framework. Furthermore, we reconfigure a loss function by combining a softmax loss and a center loss to classify the emotions. For validating our proposed methods, we use the EmoDB dataset that consists of several emotions with imbalanced samples. Experimental results prove that the proposed methods achieve better accuracy

Stats

• citations50
• S2 citations—
• github stars0
• HF likes0
• heat score12.81
• arxiv keypham2021hybrid

Related papers

• Augmenting Generative Adversarial Networks For Speech Emotion Recognition (2020)10.85
• A Breakthrough In Speech Emotion Recognition Using Deep Retinal Convolution Neural Networks (2017)0.00
• Speech Emotion Recognition With Multiscale Area Attention And Data Augmentation (2021)13.65
• Generative Data Augmentation Guided By Triplet Loss For Speech Emotion Recognition (2022)3.58
• Leveraged Mel Spectrograms Using Harmonic And Percussive Components In Speech Emotion Recognition (2023)9.03
• Improved Speech Emotion Recognition Using Transfer Learning And Spectrogram Augmentation (2021)12.74
• Emodiarize: Speaker Diarization And Emotion Identification From Speech Signals Using Convolutional Neural Networks (2023)0.00
• Generative Emotional AI For Speech Emotion Recognition: The Case For Synthetic Emotional Speech Augmentation (2023)11.19