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Speech Emotion Recognition Using Machine Learning: A Comparative Analysis
It is possible to identify emotions based on a person's speech. The field of research focusing on expressing emotions through voice is continuously...
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Multimodal speech emotion recognition based on multi-scale MFCCs and multi-view attention mechanism
In recent years, speech emotion recognition (SER) increasingly attracts attention since it is a key component of intelligent human-computer...
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DialogueINAB: an interaction neural network based on attitudes and behaviors of interlocutors for dialogue emotion recognition
Machines can be equipped with the capability of identifying human emotions through conversation, thus enabling them to empathize with natural persons...
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Multi-level attention fusion network assisted by relative entropy alignment for multimodal speech emotion recognition
Multimodal speech emotion recognition can utilize features from different modalities simultaneously to improve the modeling capabilities in affective...
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Semantic-wise guidance for efficient multimodal emotion recognition with missing modalities
Emotions play an important role in human–computer interaction. Multimodal emotion recognition combines feature information from different modalities...
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A Three-stage multimodal emotion recognition network based on text low-rank fusion
Multimodal emotion recognition has achieved good results in emotion recognition tasks by fusing multimodal information such as audio, text, and...
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Hierarchical emotion recognition from speech using source, power spectral and prosodic features
Features related to the glottal closure instants (GCI) exhibit different patterns for different emotions. In this work, our main objective was to...
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Speech Emotion Recognition using Time Distributed 2D-Convolution layers for CAPSULENETS
Speech Emotion Recognition (SER) determines human emotions using linguistic and nonlinguistic features of the uttered speech. The nonlinguistic...
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A novel conversational hierarchical attention network for speech emotion recognition in dyadic conversation
Speech is one of the most fundamental mediums for human-to-human interaction, thereby playing a pivotal role in sha** the landscape of...
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HAAN-ERC: hierarchical adaptive attention network for multimodal emotion recognition in conversation
Multimodal emotional expressions affect the progress of conversation in complex ways in our lives. For multimodal emotion recognition in conversation...
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Multimodal modelling of human emotion using sound, image and text fusion
Multimodal emotion recognition and analysis are considered as an evolving field of research. The improvement of the multimodal fusion mechanism plays...
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Pairwise-Emotion Data Distribution Smoothing for Emotion Recognition
In speech emotion recognition tasks, models learn emotional representations from datasets. We find the data distribution in the IEMOCAP dataset is...
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AudioFormer: Channel Audio Encoder Based on Multi-granularity Features
To solve the problem of poor standardized feature extraction methods for speech emotion recognition tasks and insufficient depth representation...
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DBT: multimodal emotion recognition based on dual-branch transformer
There are very few labeled datasets in speech emotion recognition. The reason is that emotion is subjective and requires much time for labeling...
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Time-Frequency Transformer: A Novel Time Frequency Joint Learning Method for Speech Emotion Recognition
In this paper, we propose a novel time-frequency joint learning method for speech emotion recognition, called Time-Frequency Transformer. Its...
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Speech Emotion Recognition Method Based on Cross-Layer Intersectant Fusion
Speech emotion recognition (SER) is a key technology in human-computer interaction (HCI) systems. Although the existing neural-based methods have...
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Speech Emotion Recognition Using Cascaded Attention Network with Joint Loss for Discrimination of Confusions
Due to the complexity of emotional expression, recognizing emotions from the speech is a critical and challenging task. In most of the studies, some...
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LMR-CBT: learning modality-fused representations with CB-Transformer for multimodal emotion recognition from unaligned multimodal sequences
Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion...
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MTGR: Improving Emotion and Sentiment Analysis with Gated Residual Networks
In this paper, we address the problem of emotion recognition and sentiment analysis. Implementing an end-to-end deep learning model for emotion...
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An End-to-End Transformer with Progressive Tri-Modal Attention for Multi-modal Emotion Recognition
Recent works on multi-modal emotion recognition move towards end-to-end models, which can extract the task-specific features supervised by the target...