IEMOCAP: interactive emotional dyadic motion capture database
PulseAugur coverage of IEMOCAP: interactive emotional dyadic motion capture database — every cluster mentioning IEMOCAP: interactive emotional dyadic motion capture database across labs, papers, and developer communities, ranked by signal.
4 day(s) with sentiment data
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New forecasting method improves prediction of annotator votes
Researchers have developed a new method called Partial-Revelation Panel Forecasting to predict the full distribution of annotator votes on a dialogue turn, given a partial set of votes from previous turns. This approach…
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New benchmark reveals emotion shifts in speech deepfakes challenge detection
A new research paper introduces EmoShift, a benchmark designed to evaluate speech deepfake detectors against synthesized speech that changes emotion within a single utterance. The study found that these intra-utterance …
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New GeoMER framework enhances emotion recognition using dual geometry
Researchers have developed GeoMER, a novel framework for multimodal emotion recognition that combines Euclidean and hyperbolic geometry. This approach aims to better capture the hierarchical structure of emotions by mod…
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New model enhances emotion recognition with uncertainty quantification
Researchers have developed an Evidential Multimodal Transformer with Dempster-Shafer Fusion (EMT-DS) to improve emotion recognition accuracy, particularly when dealing with conflicting sensor data. This new model quanti…
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Concept bottleneck models enhance explainability in speech emotion recognition
Researchers have adapted concept bottleneck models, previously used for image classification, to speech emotion recognition (SER). This adaptation aims to improve the explainability of SER systems, particularly those ut…
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LLMs achieve new SOTA in multimodal dialogue emotion evaluation
Researchers have developed a new framework for evaluating emotions in multimodal dialogues using Large Language Models (LLMs). This approach incorporates acoustic cues as natural language descriptions, building on the S…
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Hybrid AI system optimizes emotion recognition cost and accuracy
Researchers have developed a confidence-gated hybrid system for emotion recognition in conversational AI that balances cost, latency, and accuracy. This approach uses a low-cost ensemble model for most predictions and e…
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New ReH-FUSE framework enhances multimodal emotion recognition in conversations
Researchers have developed ReH-FUSE, a novel framework designed for multimodal emotion recognition in conversations. This system intelligently fuses information from text, audio, and cross-modal interactions by learning…
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New Transformer architecture improves multimodal emotion recognition in conversations
Researchers have developed a new approach to multimodal emotion recognition in conversations by enhancing the Self-Distillation Transformer architecture. This method incorporates facial geometry alongside appearance-bas…
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New lightweight framework enhances real-time emotion recognition for surveillance
Researchers have developed RAFM-SER++, a new framework designed for lightweight multimodal emotion recognition in real-time surveillance systems. This framework utilizes an asymmetric Residual Attention Fusion Mechanism…
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New DSSM-CRF model enhances conversational speech emotion recognition
Researchers have developed a new dual-scale state-space model called DSSM-CRF for speech emotion recognition in conversations. This model separates cross-speaker contextual influence from within-speaker emotion evolutio…
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New framework enhances multimodal emotion recognition using attention-based fusion
Researchers have developed a new framework for multimodal emotion recognition, integrating audio and visual data. The audio component uses Wav2Vec2, MFCCs, and acoustic descriptors processed by a BiLSTM, while the video…
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New SISER model improves speech emotion recognition with adversarial training
Researchers have developed SISER, a novel approach to speech emotion recognition that addresses data scarcity and speaker variability. By integrating the pre-trained wav2vec 2.0 model for feature extraction and an ECAPA…
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New PriMD Framework Enhances Emotion Recognition with Missing Data
Researchers have developed a new framework called Primitive Memory Distillation (PriMD) to improve multimodal emotion recognition (MER) systems when certain data modalities are missing. Unlike previous methods that trea…
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New research explores emotion detection in LLMs across layers and languages
Researchers are exploring new methods to enhance emotion detection in large language models (LLMs) by investigating how emotions are represented across different layers and languages. One study examines emotion expressi…
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New adapter DiaRelay enhances LLMs for emotion recognition in conversations
Researchers have developed DiaRelay, a novel adapter for Large Language Models (LLMs) designed to improve Emotion Recognition in Conversation (ERC). Unlike existing methods that use fixed context windows or re-encode en…
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New framework balances AI classification accuracy with interpretability
Researchers have developed a new framework for multimodal classification that balances accuracy with interpretability. This framework utilizes tree-based ensembles, specifically Linear Discriminant Tree (LDT), Linear Di…
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New AI models aim for deeper emotional understanding and reasoning · 5 sources tracked
Researchers are developing advanced multimodal AI models capable of understanding and reasoning about human emotions. Several new papers introduce frameworks and benchmarks for this purpose, focusing on integrating verb…
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HyPASE framework uses hyperbolic geometry for efficient LALM fine-tuning
Researchers have developed HyPASE, a novel framework that utilizes hyperbolic geometry for parameter-efficient fine-tuning of Large Audio-Language Models (LALMs) for Speech Emotion Recognition (SER). Unlike traditional …
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New AMRD method enables lightweight speech emotion recognition models
Researchers have developed Adaptive Multi-teacher Relational Distillation (AMRD), a novel method to create lightweight speech emotion recognition (SER) models suitable for on-device applications. AMRD addresses challeng…