speech-classification

Code for the Interspeech 2021 paper "AST: Audio Spectrogram Transformer".

Code for the AAAI 2022 paper "SSAST: Self-Supervised Audio Spectrogram Transformer".

Wav2Vec for speech recognition, classification, and audio classification

Official Implementation of the work "Audio Mamba: Bidirectional State Space Model for Audio Representation Learning"

Classification of 11 types of audio clips using MFCCs features and LSTM. Pretrained on Speech Command Dataset with intensive data augmentation.

This repository contains the code for the paper: "DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances"

Transformer-based model for Speech Emotion Recognition(SER) - implemented by Pytorch

It is a full-fetched web application.Based on sentiment classification, by using nltk library it predicts that a speech is how much toxic, sever toxic, insult, obscene, threat.

Gender Classification with different Machine Learning models, using the LibriSpeech ASR dataset.

Speech-based dysarthria classification with model calibration (ECE/OE) for reliable clinical prediction

In this challenge, the goal is to learn to recognize which of several English words is pronounced in an audio recording. This is a multiclass classification task.

Speech Classification using Continuous Attention Mechanisms

A Python implementation of the Iterative Feature Normalization algorithm

CNN Based Approach for Audio File Classification. Contains Notebooks Illustrating Data Preprocessing, Feature Extraction, Model Training, & Model Inference Workflows & Overall Pipeline

This repository contains the code for the INTERSPEECH2025 paper: "Speech and Text Foundation Models for Depression Detection: Cross-Task and Cross-Language Evaluation"

A convolutional neural network for gender classification, which achieved an F1-score of 94.3% when tested on the RAVDESS dataset. Created as postgraduate coursework, the report is included. The report also discusses Sodiq Adebiy's CNN, which I'd recommend looking at to anyone interested in emotion classification.

This project implements a speech emotion classification system using neural networks and genetic algorithms for optimization. The system classifies emotions such as calm, happy, sad, angry, fearful, surprise, and disgust from speech audio using the RAVDESS dataset.

Python implementation of the article "EMOVOME Database: Advancing Emotion Recognition in Speech Beyond Staged Scenarios"

This project aims to perform Emotion Recognition in Speech using Deep Neural Networks (DNNs)

In this notebook, we aim to recognize speech commands using classification. For this purpose, we used the SPEECHCOMMANDS dataset and the deep convolutional model M5. The code is written in Python and designed for the PyTorch platform.