speaker-identification

PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.

Speaker Identification System (upto 100% accuracy); built using Python 2.7 and python_speech_features library

This repository contains audio samples and supplementary materials accompanying publications by the "Speaker, Voice and Language" team at Google.

Identifying people from small audio fragments

Simple d-vector based Speaker Recognition (verification and identification) using Pytorch

A light weight neural speaker embeddings extraction based on Kaldi and PyTorch.

Pytorch implementation of "Generalized End-to-End Loss for Speaker Verification"

This repo contains my attempt to create a Speaker Recognition and Verification system using SideKit-1.3.1

A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.

Source code for paper "Who is real Bob? Adversarial Attacks on Speaker Recognition Systems" (IEEE S&P 2021)

Pytorch implementation of Generalized End-to-End Loss for speaker verification

Deep Learning - one shot learning for speaker recognition using Filter Banks

Keras Implementation of Deepmind's WaveNet for Supervised Learning Tasks

mirror of VoxCeleb dataset - a large-scale speaker identification dataset

Kaldi based speaker verification

Voxceleb1 i-vector based speaker recognition system

Trained speaker embedding deep learning models and evaluation pipelines in pytorch and tesorflow for speaker recognition.

声纹识别(Voiceprint Recognition, VPR)，也称为说话人识别(Speaker Recognition)，有两类，即说话人辨认(Speaker Identification)和说话人确认(Speaker Verification)

Neural speaker recognition/verification system based on Kaldi and Tensorflow

⇨ The Speaker Recognition System consists of two phases, Feature Extraction and Recognition. ⇨ In the Extraction phase, the Speaker's voice is recorded and typical number of features are extracted to form a model. ⇨ During the Recognition phase, a speech sample is compared against a previously created voice print stored in the database. ⇨ The highlight of the system is that it can identify the Speaker's voice in a Multi-Speaker Environment too. Multi-layer Perceptron (MLP) Neural Network based on error back propagation training algorithm was used to train and test the system. ⇨ The system response time was 74 µs with an average efficiency of 95%.

Keras + pyTorch implimentation of "Deep Learning & 3D Convolutional Neural Networks for Speaker Verification"

Implementation of the paper "Attentive Statistics Pooling for Deep Speaker Embedding" in Pytorch

DropClass and DropAdapt - repository for the paper accepted to Speaker Odyssey 2020

Implementation of a speaker identification and a speaker verification system based on Gaussian Mixture Models (GMM) in combination with and Universal Background Model (UBM) on the YOHO dataset in MATLAB.

UPC Deep Learning for Speech and Language 2018

Implementing VGGVox for Speaker Identification on VoxCeleb1 dataset in PyTorch.

MobileNet trained with VoxCeleb dataset and used for voice verification

Denoising autoencoders for speaker identification on MCE 2018 challenge

Source Code for 'SECurity evaluation platform FOR Speaker Recognition' released in 'Defending against Audio Adversarial Examples on Speaker Recognition Systems'