#speaker recognition

[derekallanboman]

Why Identity Resolution Is Different from Speaker Recognition

Speaker recognition and identity resolution are related, but they solve different problems.

Speaker recognition asks: Does this voice match someone we’ve heard before?

Identity resolution asks a broader question: Who is actually participating in this conversation, and how confident are we?

In real-world outbound communication, a phone number can be shared, forwarded, reassigned, or answered by an assistant, family member, or coworker. A conversation may begin before the system has enough information to determine whether it has reached the intended person.

That is why I view identity resolution as a communication-systems problem rather than simply a voice-recognition problem. Voice characteristics can contribute to confidence, but so can conversational context, prior interactions, and other signals gathered during the exchange.

This is one of the concepts behind ChannelRoute AI. The goal is not simply to automate more calls, but to help communication systems make better decisions about who is actually participating in a conversation and how to proceed.

#ChannelRouteAI #ArtificialIntelligence #CommunicationSystems #IdentityResolution #SpeakerRecognition

[botbark]

Speaker Recognition Problem uses what Deep Learning Algorithms

Speaker Recognition Problem uses what Deep Learning Algorithms

Automatic Speaker Recognition uses what Deep Learning Algorithms is a short video to discuss the Deep Learning Algorithms used for Automatic Speaker Recognition. This can be used for Machine Learning, Deep Learning, or neural network interview question. Speaker Recognition is the process of identifying a speaker by analyzing the spectral shape of the voice signal. This is done by extracting &…

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[ijtsrd]

A Robust Speaker Identification System

by Zaw Win Aung" A Robust Speaker Identification System" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, 

URL: http://www.ijtsrd.com/papers/ijtsrd18274.pdf 

Direct URL: http://www.ijtsrd.com/other-scientific-research-area/other/18274/a-robust-speaker-identification-system/zaw-win-aung

ugc journal list, call for paper papers in journals, research papers

This paper is aimed to implement a robust speaker identification system. It is a software architecture which identifies the current talker out of a set of speakers. The system is emphasized on text-dependent speaker identification system. It contains three main modules: endpoint detection, feature extraction and feature matching. The additional module, endpoint detection, removes unwanted signal and background noise from the input speech signal before subsequent processing. In the proposed system, Short-Term Energy analysis is used for endpoint detection. Mel-frequency Cepstrum Coefficients (MFCC) is applied for feature extraction to extract a small amount of data from the voice signal that can later be used to represent each speaker. For feature matching, Vector Quantization (VQ) approach using Linde, Buzo and Gray (LBG) clustering algorithm is proposed because it can reduce the amount of data and complexity. The experimental study shows that the proposed system is more robust than using the original system and it is faster in computation than the existing one. To implement this system MATLAB is used for programming. 

[ijtsrd]

Automatic Attendance System Using Speaker Recognition

by Dr. Zaw Win Aung "Automatic Attendance System Using Speaker Recognition"

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018,

URL: http://www.ijtsrd.com/papers/ijtsrd18763.pdf

Direct Link: http://www.ijtsrd.com/other-scientific-research-area/other/18763/automatic-attendance-system-using-speaker-recognition/dr-zaw-win-aung

call for paper international journal, ugc list of journals, best international journal

The main aim of this paper is to develop automatic attendance system using speaker recognition technique. The proposed system is software architecture which allows the user to access the system by making an utterance from microphone and the attendance of corresponding user is marked in the Microsoft Office Excel. The proposed system automates the whole process of taking attendance. The system uses text dependent open-set speaker identification with MFCC features and vector quantization based speaker modeling for authenticating the user. A simple Euclidean distance scoring is used as the classifier. For decision making, the new approach, mean value threshold is proposed to optimize the system performance. The database consists of 60 speech samples which were collected from 20 speakers consisting of 10 male speakers and 10 female speakers. The experimental study shows that the proposed system with mean value threshold achieves better results in recognition accuracy and verification performance than the system with other threshold setting.

[seonhooh]

Speaker Recognition

Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

Tensorflow Speech Recognition

Caffe-Speaker-Recognition