Voice Emotion Recognition of Audio (VERA) is an open-source project created for the Data Science track for the program CUNY Tech Prep (CTP) in Cohort 8. This is the 2nd deployed version of VERA. 🔊

😎 Awesome lists about Speech Emotion Recognition

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

Official code repository for the paper "Enhancing Speech Emotion Recognition through Knowledge Distillation" (ICTC 2024).

VR based simulation platform for soft skills training, integrating AI-driven feedback from the Google Gemini API to track and enhance user performance. It provides real-time, personalized insights to improve communication and professional skills.

Multilingual Voice Understanding Model

Code for the paper: Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features

Speech Emotion Classification using Legendre Memory Units (LMU)

A collection of dataset consists of a total of 8 English speech datasets for SER

Context and System Fusion in Post-ASR Emotion Recognition with Large Language Models | Submission to GenSEC Task 3 (LLM-Based Post ASR Speech Emotion Recognition Challenge)

Voice Emotion Recognition of Audio (VERA) is an open-source project created for the Data Science track for the program CUNY Tech Prep (CTP) in Cohort 8. 🔊

A collection of datasets for the purpose of emotion recognition/detection in speech.

The code of the paper "Common Discriminative Latent Space Learning for Cross-Domain Speech Emotion Recognition".

The SpeechBrain project aims to build a novel speech toolkit fully based on PyTorch. With SpeechBrain users can easily create speech processing systems, ranging from speech recognition (both HMM/DNN and end-to-end), speaker recognition, speech enhancement, speech separation, multi-microphone speech processing, and many others.

Speech emotion recognition with PyTorch

This project is about Speech Emotion Recognition using machine learning models

Multimodal Speech Emotion Recognition ViT (AST) for audio encoder and Multiscale Attention Net (MANet) for visual encoder

语音感情识别

SER Evals: In-Domain and Out-of-Domain Benchmarking for Speech Emotion Recognition

SER for emotions recognition - CNN & NCF for recommendations