Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation

• URL: http://arxiv.org/abs/2102.01813v1
• Date: Wed, 3 Feb 2021 00:39:09 GMT
• Title: Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation
• Authors: Mingke Xu, Fan Zhang, Xiaodong Cui, Wei Zhang
• Abstract summary: We apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities. To deal with data sparsity, we conduct data augmentation with vocal tract length perturbation. Experiments are carried out on the Interactive Emotional Dyadic Motion Capture dataset.
• Score: 21.163871587810615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Speech Emotion Recognition (SER), emotional characteristics often appear in diverse forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER are usually optimized on a fixed attention granularity. In this paper, we apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities and therefore the classifier can benefit from an ensemble of attentions with different scales. To deal with data sparsity, we conduct data augmentation with vocal tract length perturbation (VTLP) to improve the generalization capability of the classifier. Experiments are carried out on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved 79.34% weighted accuracy (WA) and 77.54% unweighted accuracy (UA), which, to the best of our knowledge, is the state of the art on this dataset.

Related papers

• CAST-Phys: Contactless Affective States Through Physiological signals Database [74.28082880875368]
  The lack of affective multi-modal datasets remains a major bottleneck in developing accurate emotion recognition systems.<n>We present the Contactless Affective States Through Physiological Signals Database (CAST-Phys), a novel high-quality dataset capable of remote physiological emotion recognition.<n>Our analysis highlights the crucial role of physiological signals in realistic scenarios where facial expressions alone may not provide sufficient emotional information.
  arXiv  Detail & Related papers  (2025-07-08T15:20:24Z)
• Toward Efficient Speech Emotion Recognition via Spectral Learning and Attention [0.5371337604556311]
  Speech Emotion Recognition (SER) traditionally relies on auditory data analysis for emotion classification.<n>We use Mel-Frequency Cepstral Coefficients (MFCCs) as spectral features to bridge the gap between computational emotion processing and human auditory perception.<n>We propose a novel 1D-CNN-based SER framework that integrates data augmentation techniques.
  arXiv  Detail & Related papers  (2025-07-04T01:55:49Z)
• VAEmo: Efficient Representation Learning for Visual-Audio Emotion with Knowledge Injection [50.57849622045192]
  We propose VAEmo, an efficient framework for emotion-centric joint VA representation learning with external knowledge injection.<n>VAEmo achieves state-of-the-art performance with a compact design, highlighting the benefit of unified cross-modal encoding and emotion-aware semantic guidance.
  arXiv  Detail & Related papers  (2025-05-05T03:00:51Z)
• Modelling Emotions in Face-to-Face Setting: The Interplay of Eye-Tracking, Personality, and Temporal Dynamics [1.4645774851707578]
  In this study, we showcase how integrating eye-tracking data, temporal dynamics, and personality traits can substantially enhance the detection of both perceived and felt emotions. Our findings inform the design of future affective computing and human-agent systems.
  arXiv  Detail & Related papers  (2025-03-18T13:15:32Z)
• Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition [60.58049741496505]
  Speech Emotion Recognition (SER) plays a crucial role in enhancing human-computer interaction. We propose a novel approach HuMP-CAT, which combines HuBERT, MFCC, and prosodic characteristics. We show that, by fine-tuning the source model with a small portion of speech from the target datasets, HuMP-CAT achieves an average accuracy of 78.75%.
  arXiv  Detail & Related papers  (2025-01-06T14:31:25Z)
• Smile upon the Face but Sadness in the Eyes: Emotion Recognition based on Facial Expressions and Eye Behaviors [63.194053817609024]
  We introduce eye behaviors as an important emotional cues for the creation of a new Eye-behavior-aided Multimodal Emotion Recognition dataset. For the first time, we provide annotations for both Emotion Recognition (ER) and Facial Expression Recognition (FER) in the EMER dataset. We specifically design a new EMERT architecture to concurrently enhance performance in both ER and FER.
  arXiv  Detail & Related papers  (2024-11-08T04:53:55Z)
• Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversations [15.705757672984662]
  Multimodal Emotion Recognition in Conversations (MERC) is a significant development direction for machine intelligence. Many data in MERC naturally exhibit an imbalanced distribution of emotion categories, and researchers ignore the negative impact of imbalanced data on emotion recognition. We propose the Class Boundary Enhanced Representation Learning (CBERL) model to address the imbalanced distribution of emotion categories in raw data. We have conducted extensive experiments on the IEMOCAP and MELD benchmark datasets, and the results show that CBERL has achieved a certain performance improvement in the effectiveness of emotion recognition.
  arXiv  Detail & Related papers  (2023-12-11T12:35:17Z)
• EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks [0.0]
  This research explores the integration of deep learning techniques in speech emotion recognition. It introduces a framework that combines a pre-existing speaker diarization pipeline and an emotion identification model built on a Convolutional Neural Network (CNN) The proposed model yields an unweighted accuracy of 63%, demonstrating remarkable efficiency in accurately identifying emotional states within speech signals.
  arXiv  Detail & Related papers  (2023-10-19T16:02:53Z)
• A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition [11.928873764689458]
  We conduct a comprehensive evaluation of popular deep learning approaches for emotion recognition. We show that long-range dependencies in the speech signal are critical for emotion recognition. Speed/rate augmentation offers the most robust performance gain across models.
  arXiv  Detail & Related papers  (2022-11-09T17:27:03Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• An Attribute-Aligned Strategy for Learning Speech Representation [57.891727280493015]
  We propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes. Our proposed method achieves competitive performances on identity-free SER and a better performance on emotionless SV.
  arXiv  Detail & Related papers  (2021-06-05T06:19:14Z)
• Continuous Emotion Recognition via Deep Convolutional Autoencoder and Support Vector Regressor [70.2226417364135]
  It is crucial that the machine should be able to recognize the emotional state of the user with high accuracy. Deep neural networks have been used with great success in recognizing emotions. We present a new model for continuous emotion recognition based on facial expression recognition.
  arXiv  Detail & Related papers  (2020-01-31T17:47:16Z)
• Detecting Emotion Primitives from Speech and their use in discerning Categorical Emotions [16.886826928295203]
  Emotion plays an essential role in human-to-human communication, enabling us to convey feelings such as happiness, frustration, and sincerity. This work investigated how emotion primitives can be used to detect categorical emotions such as happiness, disgust, contempt, anger, and surprise from neutral speech. Results indicated that arousal, followed by dominance was a better detector of such emotions.
  arXiv  Detail & Related papers  (2020-01-31T03:11:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.