😎 Awesome lists about Speech Emotion Recognition
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Updated
Jun 19, 2024
😎 Awesome lists about Speech Emotion Recognition
S3PRL for Speech Emotion Recognition (see s3prl > downstream)
A collection of datasets for the purpose of emotion recognition/detection in speech.
Emotion Sense uses machine learning to analyze speech and understand emotions. This Project "Emotion Sense" offers hands-on experience and industry insight in speech emotion recognition and technology trends.
Code Repository for the INTERSPEECH'24 Paper - Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning
💎 A list of accessible speech corpora for ASR, TTS, and other Speech Technologies
This project is about Speech Emotion Recognition using machine learning models
[ACL 2024] Official PyTorch code for extracting features and training downstream models with emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
This project implements a Speech Emotion Recognition (SER) model using TensorFlow Lite, specifically designed for deployment on microcontrollers like the Arduino Nano BLE33. The model is trained on the RAVDESS dataset and can recognize seven emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.
A model that recognizes emotion from speech analyzes acoustic and linguistic cues to classify the speaker's emotional state.
[ICASSP 2023] Official Tensorflow implementation of "Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition".
Speech Emotion Recognition using WAV audio files
语音感情识别
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 (SER) using Deep neural networks CNN and RNN
Predicting various emotion in human speech signal by detecting different speech components affected by human emotion.
This API utilizes a pre-trained model for emotion recognition from audio files. It accepts audio files as input, processes them using the pre-trained model, and returns the predicted emotion along with the confidence score. The API leverages the FastAPI framework for easy development and deployment.
Real-time Speech Emotion Recognition
Speech Emotion Recognition (SER) using CNNs and CRNNs Based on Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs)
Transformer-based model for Speech Emotion Recognition(SER) - implemented by Pytorch
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