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therapybot3

https://github.com/theinterns808/therapybot3

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (2.5%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: theInterns808
  • License: mit
  • Language: Python
  • Default Branch: master
  • Size: 4.64 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Using three different ML models (Audio, Visual, Text), this project aims to give helpful therapy-esque feedback to military personnel.

The Model:

https://chatgpt.com/g/g-tn1grifGO-guardian-support

additional files:

https://drive.google.com/drive/folders/10XYY8atvVbA3vTNXTWvXYl9pZHQjM9CA

files:

https://drive.google.com/file/d/1FYQAXrhhfLs0zn83PKsr4NGdAEyLwTji/view?usp=sharing

https://drive.google.com/file/d/1_nV3V-03Yz9LQkhZOXvg6aMO11YWc0hK/view?usp=sharing

https://drive.google.com/file/d/1F4nxvmb90njYbTA7lZhL-3b1j8OLHurK/view?usp=sharing

By James Clark, Issac Verbrugge, and Anrric Xu

Owner

  • Login: theInterns808
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite it as below.
authors:
  - family-names: Abdeladim
    given-names: Fadheli
title: Speech Emotion Recognition
version: 1.0.0
date-released: 2019-04-28
abstract: "This repository presents a comprehensive SER framework that employs various machine learning and deep learning techniques to accurately detect and classify human emotions from speech. The framework utilizes four datasets, including RAVDESS, TESS, EMO-DB, and a custom dataset, comprising a diverse range of emotions such as neutral, calm, happy, sad, angry, fear, disgust, pleasant surprise, and boredom. Feature extraction is performed using widely adopted audio features, including MFCC, Chromagram, MEL Spectrogram Frequency, Contrast, and Tonnetz. The repository also supports grid search for hyperparameter tuning and offers a range of classifiers and regressors such as SVC, RandomForest, GradientBoosting, KNeighbors, MLP, Bagging, and Recurrent Neural Networks. The developed SER system demonstrates promising accuracy in emotion classification, making it a valuable tool for researchers and practitioners in the field of affective computing and related domains."
repository-code: https://github.com/x4nth055/emotion-recognition-using-speech
license: MIT

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Dependencies

requirements.txt pypi
  • deepface *
  • opencv-python *