用于存放自然语言处理相关的代码。Store code related to NLP (Natural Language Processing).
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Updated
Sep 19, 2019 - Jupyter Notebook
用于存放自然语言处理相关的代码。Store code related to NLP (Natural Language Processing).
The implementation of the paper, ``Speech Emotion Recognition with Fusion of Acoustic- and Linguistic-Feature-Based Decisions'' (pretraining-based part, acoustic features)
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.
A Django web application to detect emotion from the audio speech of a recording
The Real-Time Speech Emotion Recognition Bot leverages OpenAI Whisper, Streamlit to analyze and identify emotions in spoken language in real-time.
This code is a fully functional speech emotion recognition algorithm
Official code for A Segment Level Approach to Speech Emotion Recognition using Transfer Learning, ACPR 2019
A script extracting features of emotionally charged speech
SoulSyncopia: Emotion-Based Music Recommendation System
An attempt at the speech emotion recognition (SER) task on the CREMA-D dataset using TensorFlow 1D & 2D RCNN models.
Speech Emotion Recognition (SER) using CNNs and CRNNs Based on Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs)
Speech emotion recognition models for the Moody web application.
Solution for the LoopQ Prize 2022 - A speech emotion recognition ML solution
Domain-shift Aware Meta-Learning for Domain Generalization in SER
This repository contains the Speech Emotion Recognition (SER) tools developed during the development of Mário Silva's dissertation. It includes SER machine learning models and an audio pipeline to process audio in online or offline time to be used for SER classifications.
Deep Learning for Speech Emotion Recognition - Dive into the world of emotional resonance with our cutting-edge CNN-based Speech Emotion Recognition model, achieving an impressive 94% accuracy.
Champion at Brainhack TIL 2022: Team 8000SGD_CAT
Emotion Recognition using matlab (Machine Learning using SVM and Random Forest)
This project was for the pattern recognition course I studied in college. This was the beginning of dealing with neural networks and 2 CNN models were made, 1-d model and 2-d model to deal with different forms of the data, audio and image, respectively.
The PyTorch implementation of the additional temporal modeling on the DeepEmoCluster framework
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