This document summarizes research on speaker recognition technologies. It discusses how speaker recognition can be used for biometric authentication by analyzing a person's voiceprint. It reviews literature on MFCC-GMM models for text-independent speaker verification and the use of speaker recognition in biometric security systems. The document also outlines the basic components of a speaker recognition system, including enrollment, feature extraction using MFCCs, and verification through comparison to stored voice templates using algorithms like GMM.
Voice recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
This document describes how to build a simple, yet complete and representative automatic speaker recognition system. Such a speaker recognition system has potential in many security applications. For example, users have to speak a PIN (Personal Identification Number) in order to gain access to the laboratory door, or users have to speak their credit card number over the telephone line to verify their identity. By checking the voice characteristics of the input utterance, using an automatic speaker recognition system similar to the one that we will describe, the system is able to add an extra level of security.
Voice recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. This technique makes it possible to use the speaker's voice to verify their identity and control access to services such as voice dialing, banking by telephone, telephone shopping, database access services, information services, voice mail, security control for confidential information areas, and remote access to computers.
This document describes how to build a simple, yet complete and representative automatic speaker recognition system. Such a speaker recognition system has potential in many security applications. For example, users have to speak a PIN (Personal Identification Number) in order to gain access to the laboratory door, or users have to speak their credit card number over the telephone line to verify their identity. By checking the voice characteristics of the input utterance, using an automatic speaker recognition system similar to the one that we will describe, the system is able to add an extra level of security.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0076) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Deep Learning in practice : Speech recognition and beyond - MeetupLINAGORA
Retrouvez la présentation de notre Meetup du 27 septembre 2017 présenté par notre collaborateur Abdelwahab HEBA : Deep Learning in practice : Speech recognition and beyond
Deep Learning for Speech Recognition - Vikrant Singh TomarWithTheBest
Tomar discusses the components of speech recognition, the difference between deep learning for speech and images, system architecture, GMM-HMM based systems, deep neural networks in speech, tandem DNN, and hybrids. There's a lot of exciting stuff to talk about in deep learning communities.
Vikrant Singh Tomar, Founder, Fluent.ai
Speech Recognition Service (SPRS) is a mobile application capable of recognition some of voices. And due to the system compatibility, users can running this application from any mobile devices supported by android system and SPRS is designed especially for deaf people.
Voice recognition service (VRS) recognize voices in our real life especially in our home such that sound of the bell, telephone and door. VRS allows the user to many of the notifications options when you hear a specific sound previously identified where the user can choose one of the available options such as sending a message to mobile number or vibration. VRS requires minimum knowledge of how to use the mobile in order to be able to run it. VRS has Arabic, simple and user-friendly interface.
This document includes a detailed description of system requirements both functional and non-functional, design models and description, functionalities of all system objects, and system testing so it could be used as a user manual for system users.
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
The following resources come from the 2009/10 B.Sc in Media Technology and Digital Broadcast (course number 2ELE0076) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Deep Learning in practice : Speech recognition and beyond - MeetupLINAGORA
Retrouvez la présentation de notre Meetup du 27 septembre 2017 présenté par notre collaborateur Abdelwahab HEBA : Deep Learning in practice : Speech recognition and beyond
Deep Learning for Speech Recognition - Vikrant Singh TomarWithTheBest
Tomar discusses the components of speech recognition, the difference between deep learning for speech and images, system architecture, GMM-HMM based systems, deep neural networks in speech, tandem DNN, and hybrids. There's a lot of exciting stuff to talk about in deep learning communities.
Vikrant Singh Tomar, Founder, Fluent.ai
Speech Recognition Service (SPRS) is a mobile application capable of recognition some of voices. And due to the system compatibility, users can running this application from any mobile devices supported by android system and SPRS is designed especially for deaf people.
Voice recognition service (VRS) recognize voices in our real life especially in our home such that sound of the bell, telephone and door. VRS allows the user to many of the notifications options when you hear a specific sound previously identified where the user can choose one of the available options such as sending a message to mobile number or vibration. VRS requires minimum knowledge of how to use the mobile in order to be able to run it. VRS has Arabic, simple and user-friendly interface.
This document includes a detailed description of system requirements both functional and non-functional, design models and description, functionalities of all system objects, and system testing so it could be used as a user manual for system users.
From Voice Biometrics Conference San Francisco (May 8-9, 2013): A single factor is seldom enough to establish high confidence levels when identifying a specific individual. Learn how the security services in Ecuador specified and then implemented a system that combines voice biometrics and facial recognition in a unique way to support their law enforcement efforts. -- Alexey Khitrov, Strategic Development Director, Speech Technology Center
A Combined Voice Activity Detector Based On Singular Value Decomposition and ...CSCJournals
voice activity detector (VAD) is used to separate the speech data included parts from silence parts of the signal. In this paper a new VAD algorithm is represented on the basis of singular value decomposition. There are two sections to perform the feature vector extraction. In first section voiced frames are separated from unvoiced and silence frames. In second section unvoiced frames are silence frames. To perform the above sections, first, windowing the noisy signal then Hankel’s matrix is formed for each frame. The basis of statistical feature extraction of purposed system is slope of singular value curve related to each frame by using linear regression. It is shown that the slope of singular values curve per different SNRs in voiced frames is more than the other types and this property can be to achieve the goal the first part can be used. High similarity between feature vector of unvoiced and silence frame caused to approach for separation of the two categories above cannot be used. So in the second part, the frequency characteristics for identification of unvoiced frames from silent frames have been used. Simulation results show that high speed and accuracy are the advantages of the proposed system.
Cancellation of Noise from Speech Signal using Voice Activity Detection Metho...ijsrd.com
Speech Enhancement by suppressing uncorrelated acoustically added noise has been a challenging topic of research for many years. These are the primary choice for real time applications due to the simplicity and comparatively low computational load. This paper shows VAD (Voice activity detection) technique that can detect the non speech segment from the speech signal. It is also shown that it can work powerfully in an unpredictable noise ambience. The technique is mostly done in microprocessors or DSP processors because of their flexibility. But there are several advantages of FPGA over DSP processors like high cost per logic element related to these processors makes them improper for large scale use. From the experimental results, VAD method is implemented on the FPGA chip.
transportation is the back bone of countries economical development. providing good and effective transportation facility will help in developing countries economy. transportation will save time, money, and work can be done easily. intelligent transportation system provides more benefits to the nation.
The developments in the agricultural field are the buzzword in the market. In the field of agriculture, use of proper method of irrigation is important and it is well known that irrigation by drip is very economical and efficient. In the conventional drip irrigation system, the farmer has to keep watch on irrigation timetable, which is different for different crops and it is very difficult. This paper mainly focuses on designing of an accurate & cost effective Global System for Mobile (GSM) Based Automatic Drip Irrigation System using micro-controller. In order to fulfill these objectives we have used relay and solenoid valve along with a 16×2 Liquid Crystal Display (LCD) that can be connected to the microcontroller, which will displays the soil moisture level and ambient temperature. The developed irrigation method removes the need for workmanship for flooding irrigation. Efficient water management plays an important role in the irrigated agricultural cropping systems. Time based control mechanism; volume based control mechanism and priority based mechanism can be designed in one system.
The Solar Roadway generates electrical power from the sun and becomes a nation’s decentralized, intelligent, self-healing power grid, replacing our current deteriorating power distribution infrastructure.
Intelligent Transportation System (ITS) is the modern technique implemented in many developed countries and is under implementation in developing countries like india.This presentation gives a brief idea about ITS.
Identity authentication using voice biometrics techniqueeSAT Journals
Abstract
Identification of people using name, appearances, badges, tags and register may be effective may be in a small organization.
However, as the size of the organization or society increases, these simple ways of identifying individual become ineffective.
Therefore, it may be necessary to employ additional and more sophisticated means of authenticating the identity of people as the
population increases. Voice Biometrics is a method by which individuals can be uniquely identified by evaluating one or more
distinguishing biological traits associated with the voice of such individuals. In this paper, an unconstrained text-independent
recognition system using the Gaussian Mixture Model was applied to match recorded voice to stored voice for the purpose of
identification of individual. Recorded voices were processed and stored in the enrollment phase while probing voices were used
for comparison in the verification/recognition phase of the system.
Keywords: Model, Biometric, verification, enrollment, database, authentication, matching, identity.
Bhusan Chettri explains how we can use unique VOICE for automatic authentication and given an overview of challenges towards the security of voice authentication systems.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using back propagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Utterance Based Speaker Identification Using ANNIJCSEA Journal
In this paper we present the implementation of speaker identification system using artificial neural network with digital signal processing. The system is designed to work with the text-dependent speaker identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using an audio wave recorder. The speech features are acquired by the digital signal processing technique. The identification of speaker using frequency domain data is performed using backpropagation algorithm. Hamming window and Blackman-Harris window are used to investigate better speaker identification performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
Real Time Speaker Identification System – Design, Implementation and ValidationIDES Editor
This paper presents design, implementation and
validation of a PC based Prototype speaker recognition and
verification system. This system is organized to receive speech
signal, find the features of speech signal, and recognize and
verify a person using voice as the biometric. The system is
implemented to capture the speech signal from microphone
and to compare it with the stored data base using filter-bank
based closed-set speaker verification system. At first, the
identification of the voice signals is done using an algorithm
developed in MATLAB. Next, a PC based prototype system is
developed and is validated in real time. Several tests were made
on different sets of voice signals, and measured the performance
and the speed of the proposed system in real environment. The
result confirmed the use of proposed system for various real
time applications.
Authentication System Based on the Combination of Voice Biometrics and OTP Ge...ijtsrd
Authentication is the process by which the identity of an individual is verified. Voice authentication is the verification of identity based on the analysis of an individual's voice. Voice authentication has various advantages, but it is seldom implemented due its shortcomings as compared to other forms of biometric authentication. In this paper we have discussed about the approach for the implementation of voice authentication system through the combination of OTP to increase its real world applicability and reduce its shortcomings. Tridib Mondal | Praveen Kumar Pandey "Authentication System Based on the Combination of Voice Biometrics and OTP Generation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31595.pdf Paper Url :https://www.ijtsrd.com/computer-science/computer-security/31595/authentication-system-based-on-the-combination-of-voice-biometrics-and-otp-generation/tridib-mondal
Forensic and Automatic Speaker Recognition System IJECEIAES
Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of a contextual bias or pre-centric understanding of a reference model with the validity of an unknown audio s ample and any suspicious individual. Many researchers are continuing to develop automatic algorithms in signal processing and machine learning so that improving performance can effectively introduce the speaker’s identity, where the automatic system performs equally with the human audience. In this paper, I examine the literature about the identification of speakers by machines and humans, emphasizing the key technical speaker pattern emerging for the automatic technology in the last decade. I focus on many aspects of automatic speaker recognition (ASR) systems, including speaker-specific features, speaker models, standard assessment data sets, and performance metrics.
In this paper we present the implementation of speaker identification system using artificial neural network
with digital signal processing. The system is designed to work with the text-dependent speaker
identification for Bangla Speech. The utterances of speakers are recorded for specific Bangla words using
an audio wave recorder. The speech features are acquired by the digital signal processing technique. The
identification of speaker using frequency domain data is performed using backpropagation algorithm.
Hamming window and Blackman-Harris window are used to investigate better speaker identification
performance. Endpoint detection of speech is developed in order to achieve high accuracy of the system.
An overview of speaker recognition by Bhusan Chettri.pdfBhusan Chettri
In this article, Bhusan Chettri provides an overview of voice authentication system that is based on automatic speaker verification technology. He provides background on both the traditional approaches of modelling speakers and current deep learning based approaches. A brief introduction to how these systems can be manipulated is also provided.
Classification of Language Speech Recognition Systemijtsrd
This paper is aimed to implement Classification of Language Speech Recognition System by using feature extraction and classification. It is an Automatic language Speech Recognition system. This system is a software architecture which outputs digits from the input speech signals. The system is emphasized on Speaker Dependent Isolated Word Recognition System. To implement this system, a good quality microphone is required to record the speech signals. This system contains two main modules feature extraction and feature matching. Feature extraction is the process of extracting a small amount of data from the voice signal that can later be used to represent each speech signal. Feature matching involves the actual procedure to identify the unknown speech signal by comparing extracted features from the voice input of a set of known speech signals and the decision making process. In this system, the Mel frequency Cepstrum Coefficient MFCC is used for feature extraction and Vector Quantization VQ which uses the LBG algorithm is used for feature matching. Khin May Yee | Moh Moh Khaing | Thu Zar Aung "Classification of Language Speech Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26546.pdfPaper URL: https://www.ijtsrd.com/computer-science/speech-recognition/26546/classification-of-language-speech-recognition-system/khin-may-yee
In this article, Bhusan Chettri provides an overview of person authentication system that is based on automatic speaker verification technology. He provides background on both the traditional approaches of modelling speakers and current deep learning based approaches. A brief introduction to how these systems can be manipulated is also provided.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
VOICE BIOMETRIC IDENTITY AUTHENTICATION MODEL FOR IOT DEVICESijsptm
Behavioral biometric authentication is considered as a promising approach to securing the internet of things (IoT) ecosystem. In this paper, we investigated the need and suitability of employing voice recognition systems in the user authentication of the IoT. Tools and techniques used in accomplishing voice recognition systems are reviewed, and their appropriateness to the IoT environment are discussed. In the end, a voice recognition system is proposed for IoT ecosystem user authentication. The proposed system has two phases. The first being the enrollment phase consisting of a pre-processing step where the noise is removed from the voice for the enrollment process, the feature extraction step where feature traits are extracted from user’s voice, and the model training step where the voice model is trained for the IoT user. And the second being the phase verifies whether the identity claimer is the owner of the IoT device. Based on the resources limitedness of the IoT technologies, the suitability of text-dependent voice recognition systems is promoted. Likewise, the use of MFCC features is considered in the proposed system.
1. Submitted by : ABHINAV TYAGI (9911103403)
ANSHULI MITTAL(9911103436)
2. Introduction
Automatic speaker recognition is the use of a software to
recognize a person from a spoken phrase. These software can
operate in two modes: to identify a particular person or to
verify a person’s claimed identity.
Speaker recognition is a performance biometric; i.e., you
perform a task to be recognized. Your voice, like other
biometrics, cannot be forgotten or misplaced, unlike
knowledge-based (e.g., password) or possession-based (e.g.,
key) access control methods.
3. Literature Survey
SPEAKER RECOGNITION USING MFCC AND GMM
Author : Ashutosh Parab, JoyebMulla, PankajBhadoria, and
VikramBangar, University of Pune
Biometric is physical characteristic unique to each individual. Due to the
increased number of dialogue system applications the interest in that field
has grown significantly in recent years. Nevertheless, there are many open
issues in the field of automatic speaker identifi-cation. Among them the
choice of the appropriate speech signal features and machine learning
algorithms could be mentioned.
We have also studied and compared different approaches and algorithms
to find out the most efficient model for speaker recognition. We believe
MFCC-GMM model is most appropriate based on parameters like
identification accuracy, computation time, false rejection rate, false accep-
tance rate. The proposed system is a version of voice bio metric which
incorporates text independent speaker verifica-tion implemented
independently.
4. SPEAKER RECOGNITION IN THE BIOMETRIC SECURITY SYSTEMS
Author: Filip Ors´ag, Faculty of Information Technology Institute of Intelligent
Systems
At present, the importance of the biometric security increases a lot in
context of the events in the world. Development of the individual
biometric technologies such as the fingerprint recognition, iris or retina
recognition or speaker recognition has been considered very important.
However, it comes to be true that only one biometric technology is not
sufficient enough. Herein a design of the complex biometric security
system is introduced based on the speaker recognition and the fingerprint
authentication. A method of acquisition of a unique vector from speaker
specific features is introduced as well.
5. SPEAKER RECOGNITION
Author : Joseph P. Campbell, Jr. (j.campbell@ieee.org)
A tutorial on the design and development of automatic
speaker recognition systems is presented. Automatic
speaker recognition is the use of a machine to recognize a
person from a spoken phrase. These systems can operate
in two modes: to identify a particular person or to verify a
person’s claimed identity. Speech processing and the basic
components of automatic speaker recognition systems are
shown and design tradeoffs are discussed. The
performances of various systems are compared.
6. Problem Statement
Today security is the most important aspect for a person. At
banks, hospitals, offices a person may not be physically
present but his id, passwords, keys can be illegally used to
operate on. Thus a much secure software is needed for
security at these places.
7. Solution
"Biometrics" means "life measurement" but the term is usually associated
with the use of unique physiological characteristics to identify an
individual. A number of biometric traits have been developed and are used
to authenticate the person's identity.
The method of identification based on biometric characteristics is
preferred over traditional passwords and PIN based methods for various
reasons such as: The person to be identified is required to be physically
present at the time-of-identification. Identification based on biometric
techniques obviates the need to remember a password or carry a token. A
biometric system is essentially a pattern recognition system which makes a
personal identification by determining the authenticity of a specific
physiological or behavioural characteristic possessed by the user.
During Capture process, raw biometric is captured by a sensing device
such as a fingerprint scanner or video camera. Among the various
biometric technologies being considered, the attributes which satisfy the
above requirements are fingerprint, facial features, hand geometry, voice,
iris, retina, vein patterns, palm print, DNA, keystroke dynamics, ear shape,
odor, signature etc.
8.
9. Speaker verification is defined as deciding if a speaker is who he claims to
be. This is different than the speaker identification problem, which is
deciding if a speaker is a specific person or is among a group of persons.
In speaker verification, a person makes an identity claim (e.g., entering an
employee number or presenting his smart card). In text-dependent
recognition, the phrase is known to the system and it can be fixed or not
fixed and prompted (visually or orally). This signal is analyzed by a
verification system that makes the binary decision to accept or reject the
user’s identity claim or possibly to report insufficient confidence and
request additional input before making the decision. He then attempts to
be authenticated by speaking a prompted phrase(s) into the microphone.
There is generally a tradeoffs between recognition accuracy and the test-
session duration of speech. In addition to his voice, ambient room noise
and delayed versions of his voice enter the microphone via reflective
acoustic surfaces. Prior to a verification session, users must enrol in the
system (typically under supervised conditions). During this enrolment,
voice models are generated and stored (possibly on a smart card) for use in
later verification sessions. There is also generally a trade off between
recognition accuracy and the enrolment-session duration of speech and the
number of enrolment sessions.
10. Protocols And Algorithms
Text-dependent algorithm: The text-dependent speaker
recognition is based on saying the same phrase for enrollment
and verification. If a voice sample matches the template that was
extracted from a specific phrase.
Two-factor authentication with a passphrase. Each user records
a unique phrase (such as passphrase or an answer to a “secret
question” that is known only by the person being enrolled).
Text-independent algorithm. This method is more convenient, as
it does not require each user to remember the passphrase.
Automatic voice activity detection. Detect when users start and
finish speaking.
Liveness detection. A system may request each user to enroll a
set of unique phrases. Later the user will be requested to say a
specific phrase from the enrolled set.
11. Identification capability. VeriSpeak functions can be used
in 1-to-1 matching (verification) and 1-to-many
(identification) modes.
Multiple samples of the same phrase. A template may
store several voice records with the same phrase to improve
recognition reliability.
Fused matching. A system may ask users to pronounce
several specific phrases during speaker verification or
identification and match each audio sample against records
in the database.
12. Text Independent Algorithm
This method involves the training of speech patterns and
recognition of patterns via pattern comparison. This type
of characterization of speech via training is called pattern
classification.
1.Compute power spectrum of windowed speech.
2. Perform grouping to 21 critical bands in bark scale or mel
scale for sampling frequency of 16 kHz.
3. Perform loudness equalization and cube root compression
to simulate the power law of hearing.
4. Perform IFFT
5. Perform LP analysis by Levinson -Durbin procedure.
6. Convert LP coefficients into cepstral coefficients.
13.
14. The way in which L training vectors can be clustered into a set of
M code book vectors is by K-means clustering algorithm.
Clusters are formed in such a way that they capture the
characteristics of the training data distribution. It is observed that
Euclidean distance is small for the most frequently occurring
vectors and large for the least frequently occurring ones.