RU directed speech classifier (ruElectra, synthetic ASR noise)

Detecting Speed and Tempo Alterations in Speech Recordings

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.

Yamnet for speech classification using CPP and ONNX-runtime-2025高通边缘智能创新应用大赛入围决赛方案

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

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)

A database of challenging voice utterances collected by the Biometrics Vision and Computing (BVC) group.

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

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.

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

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

Qafar-af and Amharic voice Command Recognition project to control the movement of wheelchair

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

This project represents my research on dementia classification using audio data.

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.

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"

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

Speech Classification using Continuous Attention Mechanisms