iemocap

[ACL 2024] Official PyTorch code for extracting features and training downstream models with emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation

Lightweight and Interpretable ML Model for Speech Emotion Recognition and Ambiguity Resolution (trained on IEMOCAP dataset)

[ICASSP 2023] Official Tensorflow implementation of "Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition".

Code for Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Information

Code for the InterSpeech 2023 paper: MMER: Multimodal Multi-task learning for Speech Emotion Recognition

Modality-Transferable-MER, multimodal emotion recognition model with zero-shot and few-shot abilities.

多模态情绪识别方法研究（Multimodal Emotion Recognition）

A Tensorflow implementation of Speech Emotion Recognition using Audio signals and Text Data

[ECAI 2023] Official implementation of "FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion Recognition"

for my ai lab

CNN for language-independent emotion prediction on a dataset of English and French speech samples.

Emotion recognition from Speech & Text using different heterogeneous ensemble learning methods

speech emotion recognition, augmentation

HGFM : A Hierarchical Grained and Feature Model for Acoustic Emotion Recgnition

Multimodal emotion recognition framework for Human–Robot Interaction, featuring adaptive cross-modal fusion and emotion-level Transformer decoding with built-in interpretability.

Comparing text-only (RoBERTa), audio-only (wav2vec2/WavLM), and multimodal transfer learning approaches for Speech Emotion Recognition on RAVDESS, MELD, and IEMOCAP. PyTorch Lightning + HuggingFace + Gradio demo.

Codes for extracting features of IEMOCAP with Open Smile tool kit

Road towards multi reward RL for emotion classification from IEMOCAP Dataset

This project explores facial emotion recognition by combining spatial and temporal learning in a structured two-stage pipeline. The motivation was to move beyond static image-based classification and build a system capable of understanding how emotions evolve over time in real-world scenarios.

Emotion Recognition with Pruning Support