A speaker recognition/identification system written in Python, based on the LIUM Speaker Diarization framework.VoiceID can process video or audio files to identify in which slices of time there is a person speaking (diarization); then it examines all those segments to identify who is speaking. To...
本项目是基于VGG-Speaker-Recognition开发的,本项目主要是用于声纹识别,也有人称为说话人识别。本项目包括了自定义数据集的训练,声纹对比,和声纹识别。 本教程源码:https://github.com/yeyupiaoling/Kersa-Speaker-Recognition 安装环境 本项目使用的是Python 3.7,Keras2.3.1和Tensorflow1.15.3,音频处理使用librosa库...
官方Python SDK: https://github.com/Microsoft/Cognitive-SpeakerRecognition-Python 第三方封装库:https://github.com/robladbrook/ms-cognitive-speaker-recognition Request URL https://westus.api.cognitive.microsoft.com/spid/v1.0/ + xxx verification & identification text-dependent text-independent .wav文件格...
蘑菇炖提莫:基于深度学习的声纹识别概述(Speaker Recognition Based on Deep Learning: An Overview)105 赞同 · 13 评论文章 本文中为了训练方便,采用的是经典的x-vector模型,该模型是d-vector的重要演变,它首先通过TDNN层提取语音帧的帧级特征,然后通过pooling将帧级特征的均值和标准差连接起来作为段级特征,最后通...
In this quickstart, you use speaker recognition to confirm who is speaking. Learn about common design patterns for working with speaker verification and identification.
speaker recognition,i-vector,MATLAB,Python,SIDEKIT,MSR Identity ToolboxThe i-vectors with Probabilistic Linear Discriminative Analysis (PLDA) are known to be one of the latest and most advanced techniques in the field of Automatic Speaker Recognition (ASR). The paper focuses on the development of ...
语音识别 (speech recognition) :说话人自适应训练 语音转换 (voice conversion) :说话人音色建模 语音合成 (speech synthesis) :说话人音色建模 目标说话人增强 (target speaker enhancement): 作为目标说话人的reference。 常见的基于神经网络的说话人表征学习框架如下图 ...
[5] D. Yu and L. Deng, Automatic Speech Recognition - A Deep Learning Approach, Springer, 2015. [6] G. Dahl, D. Yu, L. Deng, and A. Acero, Contextdependent pre-trained deep neural networks for large vocabulary speech recognition, IEEE Transactions on Audio, Speech, and Language Proces...
Several speaker recognition algorithms failed to get the best results because of the wildly varying datasets and feature sets for classification. Gender information helps reduce this effort since categorizing the classes based on gender may help lessen the impact of gender variability on the retrieved ...
import pickle # For python3 import numpy as np import configure as c from DB_wav_reader import read_DB_structure def read_MFB(filename): with open(filename, 'rb') as f: feat_and_label = pickle.load(f) feature = feat_and_label['feat'] # size : (n_frames, dim=40) ...