1) Voice biometrics refers to technology that recognizes individuals by their unique vocal characteristics. It is commonly used for automatic speaker verification (ASV) systems.
2) ASV systems have two phases - a training phase where a universal voice template is built from diverse data and adapted to create speaker-specific models, and a verification phase where a test voice is matched against templates to verify identity.
3) While voice biometrics provide advantages like password-free access, there are growing security concerns as artificially generated voices can potentially spoof systems; researchers are working on countermeasures to address this.
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.
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.
In this article Dr. Bhusan Chettri provides an overview of how voice authentication systems can be compromised through spoofing attacks. He adds "spoofing attack refers to the process of making an un-authorised attempt to break into someone else's authentication system either using synthetic voices produces through AI technology or by performing a mimicry or by simply replaying a pre-recorded voice samples of the target user."
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.
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.
In this article Dr. Bhusan Chettri provides an overview of how voice authentication systems can be compromised through spoofing attacks. He adds "spoofing attack refers to the process of making an un-authorised attempt to break into someone else's authentication system either using synthetic voices produces through AI technology or by performing a mimicry or by simply replaying a pre-recorded voice samples of the target user."
Overview on how AI-based voice authentication system can be fooled using AI.
Dr. Bhusan Chettri who earned his PhD in AI and Speech Technology from Queen Mary University of London explains how Automatic Verification Systems (ASV) can be fooled using AI
Today’s ASV system trained on big-data and complex deep learning algorithms has shown superior performance on many benchmark datasets. They have demonstrated capability of recognising a person even using a small fragment of speech utterance. However, recent research has shown that they are not 100% secure. They are prone to fraudulent access launched through so called voice spoofing attacks. An attacker with malicious intention attempts to launch spoofing attacks using either pre-recorded voice of a target speaker (Replay attack), or generating synthetic voices using technologies such as Text-to-Speech (TTS) synthesis, Voice conversion (VC). They can also launch such spoofing attacks using impersonation or mimicry and for this to succeed the attacker must be a professional in performing the act of mimicry. Figure 1 summarises different points where from attacks can be launched to fool a biometric system. Among various points of attack, the first two are of great interest as these points are more susceptible for an attacker to launch a spoofing attack. These points are generally categorised into two groups depending upon the method employed to attack:
Mr Bhusan Chettri is a researcher in AI and Machine learning applied to sound and speech technology. He earned his PhD from the prestigious Queen Mary University in London and a master's degree in speech and language processing from The University of Sheffield.
Developing a hands-free interface to operate a Computer using voice commandMohammad Liton Hossain
The main focus of this study is to help a handicap person to operate a computer by voice command. It can be used to operate the entire computer functions on the user’s voice commands. It makes use of the Speech Recognition technology that allows the computer system to identify and recognize words spoken by a human using a microphone. This Software will be able to recognize spoken words and enable user to interact with the computer. This interaction includes user giving commands to his computer which will then respond by performing several tasks, actions or operations depending on the commands they gave. For Example: Opening /closing a file in computer, YouTube automation using voice command, Google search using voice command, make a note using voice command, calculation by calculator using voice command etc.
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
Voice recognition system is a system which is used to convert human voice into signal, which can be understood by the machines. When this is achieved, the machine can be made to work, as desired. The machine could be a computer, a typewriter, or even a robot. There are systems available, in which the machine ‘speaks’ the recorded word. But that is out of the scope of this paper. Here, only the human is expected to talk. Further, the voice recognition systems described here, can be used for projects only.
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.
Online interview is not a new thing but in this covid-19 situation it seems to be the only option. However, assessing the candidate on a video call may not be that effective. Having an AI based Interview Assessment System could prove to be useful, which would take input as speech and will give output as detailed analysis of that speech. While most the research work currently done focuses only on finding sentiment or personality from speech, our system aims to extract multiple information from the speech and provide a detailed analysis. The analysis would include a detailed report containing results about confidence level of the person, his/her emotional state, speed of the speech, frequently repeated words and also personality reflected by that speech. An interview panel consists of various members focusing on different aspect of the answer given by the candidate, some focus on technical correctness while, some simply want to check the communication skills of the candidate. Having an AI system giving a report on the soft skills part would reduce the work for interviewer and he/she could give complete focus on the technical correctness of the answer. This could eventually help save time and resources used by organizations for hiring process. This intention of creating this system is to assist the interview process and give analysis report based on the speech input instead a giving a verdict about selection of the candidate. Thus, this system could use not only by the interviewers but also by the candidates. The output provided would be a detailed report which could prove to be a good feedback for the students who are preparing for the interview. Having a feedback would help candidates work on their week points and thus perform better in further interviews.
Assistive Examination System for Visually ImpairedEditor IJCATR
This paper presents a design of voice enabled examination system which can be used by the visually challenged students.
The system uses Text-to-Speech (TTS) and Speech-to-Text (STT) technology. The text-to-speech and speech-to-text web based
academic testing software would provide an interaction for blind students to enhance their educational experiences by providing them
with a tool to give the exams. This system will aid the differently-abled to appear for online tests and enable them to come at par with
the other students. This system can also be used by students with learning disabilities or by people who wish to take the examination in
a combined auditory and visual way.
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.
A Survey on Speech Recognition with Language Specificationijtsrd
As a cross disciplinary, speech recognition is entirely based on the speech as the survey object. Speech recognition allows the machine to convert the speech signal into text or commands via the process of identification and understanding. Speech recognition involves in various fields of physiology, psychology, linguistics, computer science and signal processing, and is even related to the person’s body language, and its goal is to achieve natural language communication between man and machine. The speech recognition technology is gradually becoming the key technology of the IT man machine interface. This paper describes the development of speech recognition technology and its basic principles, methods, reviewed the classification of speech recognition systems, speech recognition approaches and voice recognition technology, analyzed the problems faced by the speech recognition. Dr. Preeti Savant | Lakshmi Sandhya H "A Survey on Speech Recognition with Language Specification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49370.pdf Paper URL: https://www.ijtsrd.com/computer-science/speech-recognition/49370/a-survey-on-speech-recognition-with-language-specification/dr-preeti-savant
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Overview on how AI-based voice authentication system can be fooled using AI.
Dr. Bhusan Chettri who earned his PhD in AI and Speech Technology from Queen Mary University of London explains how Automatic Verification Systems (ASV) can be fooled using AI
Today’s ASV system trained on big-data and complex deep learning algorithms has shown superior performance on many benchmark datasets. They have demonstrated capability of recognising a person even using a small fragment of speech utterance. However, recent research has shown that they are not 100% secure. They are prone to fraudulent access launched through so called voice spoofing attacks. An attacker with malicious intention attempts to launch spoofing attacks using either pre-recorded voice of a target speaker (Replay attack), or generating synthetic voices using technologies such as Text-to-Speech (TTS) synthesis, Voice conversion (VC). They can also launch such spoofing attacks using impersonation or mimicry and for this to succeed the attacker must be a professional in performing the act of mimicry. Figure 1 summarises different points where from attacks can be launched to fool a biometric system. Among various points of attack, the first two are of great interest as these points are more susceptible for an attacker to launch a spoofing attack. These points are generally categorised into two groups depending upon the method employed to attack:
Mr Bhusan Chettri is a researcher in AI and Machine learning applied to sound and speech technology. He earned his PhD from the prestigious Queen Mary University in London and a master's degree in speech and language processing from The University of Sheffield.
Developing a hands-free interface to operate a Computer using voice commandMohammad Liton Hossain
The main focus of this study is to help a handicap person to operate a computer by voice command. It can be used to operate the entire computer functions on the user’s voice commands. It makes use of the Speech Recognition technology that allows the computer system to identify and recognize words spoken by a human using a microphone. This Software will be able to recognize spoken words and enable user to interact with the computer. This interaction includes user giving commands to his computer which will then respond by performing several tasks, actions or operations depending on the commands they gave. For Example: Opening /closing a file in computer, YouTube automation using voice command, Google search using voice command, make a note using voice command, calculation by calculator using voice command etc.
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
Voice recognition system is a system which is used to convert human voice into signal, which can be understood by the machines. When this is achieved, the machine can be made to work, as desired. The machine could be a computer, a typewriter, or even a robot. There are systems available, in which the machine ‘speaks’ the recorded word. But that is out of the scope of this paper. Here, only the human is expected to talk. Further, the voice recognition systems described here, can be used for projects only.
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.
Online interview is not a new thing but in this covid-19 situation it seems to be the only option. However, assessing the candidate on a video call may not be that effective. Having an AI based Interview Assessment System could prove to be useful, which would take input as speech and will give output as detailed analysis of that speech. While most the research work currently done focuses only on finding sentiment or personality from speech, our system aims to extract multiple information from the speech and provide a detailed analysis. The analysis would include a detailed report containing results about confidence level of the person, his/her emotional state, speed of the speech, frequently repeated words and also personality reflected by that speech. An interview panel consists of various members focusing on different aspect of the answer given by the candidate, some focus on technical correctness while, some simply want to check the communication skills of the candidate. Having an AI system giving a report on the soft skills part would reduce the work for interviewer and he/she could give complete focus on the technical correctness of the answer. This could eventually help save time and resources used by organizations for hiring process. This intention of creating this system is to assist the interview process and give analysis report based on the speech input instead a giving a verdict about selection of the candidate. Thus, this system could use not only by the interviewers but also by the candidates. The output provided would be a detailed report which could prove to be a good feedback for the students who are preparing for the interview. Having a feedback would help candidates work on their week points and thus perform better in further interviews.
Assistive Examination System for Visually ImpairedEditor IJCATR
This paper presents a design of voice enabled examination system which can be used by the visually challenged students.
The system uses Text-to-Speech (TTS) and Speech-to-Text (STT) technology. The text-to-speech and speech-to-text web based
academic testing software would provide an interaction for blind students to enhance their educational experiences by providing them
with a tool to give the exams. This system will aid the differently-abled to appear for online tests and enable them to come at par with
the other students. This system can also be used by students with learning disabilities or by people who wish to take the examination in
a combined auditory and visual way.
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.
A Survey on Speech Recognition with Language Specificationijtsrd
As a cross disciplinary, speech recognition is entirely based on the speech as the survey object. Speech recognition allows the machine to convert the speech signal into text or commands via the process of identification and understanding. Speech recognition involves in various fields of physiology, psychology, linguistics, computer science and signal processing, and is even related to the person’s body language, and its goal is to achieve natural language communication between man and machine. The speech recognition technology is gradually becoming the key technology of the IT man machine interface. This paper describes the development of speech recognition technology and its basic principles, methods, reviewed the classification of speech recognition systems, speech recognition approaches and voice recognition technology, analyzed the problems faced by the speech recognition. Dr. Preeti Savant | Lakshmi Sandhya H "A Survey on Speech Recognition with Language Specification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49370.pdf Paper URL: https://www.ijtsrd.com/computer-science/speech-recognition/49370/a-survey-on-speech-recognition-with-language-specification/dr-preeti-savant
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.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
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End-to-end overview of CI/CD pipeline with Azure devops
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Story Voice authentication systems .pdf
1. Bhusan Chettri explains how your unique VOICE can be used for automatic
authentication and challenges towards the security of voice authentication systems
· Dr. Bhusan Chettri a Ph.D graduate from Queen Mary University of London (QMUL)
explains the science behind voice biometrics; different types of voice biometric
system
s
· Spoo
fi
ng attacks on voice biometric systems: a growing concern regarding its
security
Voice biometrics in simple words refers to the technology used to automatically recognise
a person using their voice sample. Every person possesses a unique vocal apparatus and
therefore the characteristics and features of an individual's voice is distinct. This is one of
the key reasons for wide adoption of voice as a means of person authentication across the
globe. In this article, Dr Bhusan Chettri explains the basics of voice biometrics and briefs
about growing concern regarding its security against fake voices generated using
computers and AI technology
.
Voice biometrics are commonly referred to as automatic speaker veri
fi
cation (ASV). Two
key steps are required to be followed in order to build such a system using a computer.
Training phase: it involves building a universal voice template a.k.a speaker template (or
model) using large amounts of voice samples collected from different people with different
cultural background, ethnicity and from different regions across the world. The more data
recorded/collected under different diverse environmental conditions from a large speaker
population the better will be the universal template because with such diverse data the
template will be able to capture and represent the general voice pattern of speakers
across the world. Furthermore, the voice template (also referred as speaker model) is
simply a large table (or matrix) of numbers learned during the training such that each
number in the table represents some meaningful information (about the speaker) which the
computer understands but is hard for humans to interpret. As illustrated in Figure 1, top
row, this step is often called of
fl
ine phase training.
Figure 1. Training phase. The goal here is to build speaker speci
fi
c models by adaptin
g
a background model which is trained on a large speech database
.
Here, the feature extraction step simply gathers relevant information from the voice/speech
samples of speakers and use them for building the voice template. The training step then
makes use of the features being extracted from voices and applies computer algorithm to
2. learn patterns across different voices. As a results this step produces the so called
background model which is nothing but the universal speaker template representing the
whole speaker/voice population. Then the next key step in training phase is building
speaker speci
fi
c model or voice template for a designated speaker making use of the
universal speaker template. One interesting point to note here is that this step, also called
speaker or voice registration, does not require huge amount of voice samples from the
speci
fi
c target speaker. And, it is also impractical to collect thousands of hours of speech/
voice samples for one speaker. This is the reason why universal speaker/voice template
are created and are then adapted to build speaker speci
fi
c template. What this means is
that using a small fragment of voice samples (usually 5-10 seconds or a minute speech
sample) the large table (universal voice template) is adjusted to represent the speci
fi
c
speaker. It should also be noted that this speaker registration often happens on the
fl
y. For
example, in voice-based banking application, the system often ask user’s to speak certain
phrase such as “my voice is my password” for couple of times. What is happening here is
that the universal voice template is being adjusted to suit the user’s voice pattern. Once it
is successful, a voice template/model for a speci
fi
c user is created
.
Veri
fi
cation phase: The second step in voice biometrics is called speaker veri
fi
cation
phase. Here, the system accepts as input a test speech/voice sample and extracts
relevant features from it. Then the system will simply match this new speech/voice with the
voice template of the claimed speaker (which was already created during the training
phase). As a result a number/score is produced that informs the level/degree of match
being observed. Furthermore, it also uses the universal voice template to score this new
voice. Finally, the score difference between the speaker voice template and universal
voice template (also called log-likelihood ratio in ASV terminology) is used as the
fi
nal
score to decide whether to accept or reject the claimed identity. Higher score difference
usually corresponds to higher probability that the new voice sample belongs to the claimed
identity. This process is illustrated in Figure 2
.
Figure 2. Speaker veri
fi
cation phase. For a given speech utterance the system obtains a
veri
fi
cation score and makes a decision whether to accept or reject the claimed identity
.
Types of ASV systems. Depending upon the level of user cooperation ASV systems are
often classi
fi
ed into two types: text-dependent and text independent systems. In text-
dependent applications, the system has prior knowledge about the text being spoken and
therefore it expects the same utterance when the biometric system is accessed by the
3. user. An example usage of this scenario would be banking applications. On the contrary, in
text-independent systems there are no such restrictions. Users can speak any phrase
during registration and while accessing the system. An example of this would be forensic
applications where users may not be cooperating to speak the phrase they are being
asked to during interrogations
.
Bhusan Chettri further elucidated, Now, one interesting question that might pop up in the
reader's mind is regarding the usage of this technology. Where is this technology used?
What are its applications
?
Application
s
ASV systems can be used in a wide range of applications across different domains.
1. Access control: controlling access to electronic devices and other facilities using
voice
.
2. Speaker diarization applications: identifying who spoke when
?
3. Forensic application - to match voice templates with pre-recorded voices of
criminals
.
4. Retrieval of customer information in call centres using voice indexing
.
5. Surveillance applications
.
Advantages
There are many advantages to using this technology. One interesting one is the fact that
using voice biometrics user’s do not have to worry about remembering long complex
combinations of passwords anymore. By just speaking up the unlock phrase (for example,
“my voice is my password”) users can access the application (for example banking app or
personalised digital accessories)
.
Common errors in ASV
Like any other computer systems (or machine learning models) ASV systems can make
mistakes while it is up and running. There are two types of common errors it can make:
false acceptance and false rejection. False acceptance means that the system has falsely
accepted an unknown (or unregistered) speaker. False rejection is an error which refers to
a situation where the system rejects the true speaker. This may happen in cases for
example where a user attempts to access the voice biometrics in very noisy conditions
(with severe background noises), and therefore the system becomes incon
fi
dent in
recognising the speaker’s voice
.
How good is voice biometrics? Evaluation metric
s
“To decide whether the trained biometric system is good or not, an evaluation metric is
required. Commonly used metric in ASV is Equal Error Rate (EER). EER basically
corresponds to a situation where both false acceptance and false rejection errors are the
same. And for this to happen the decision threshold to accept or reject a speaker is
carefully adjusted during training (and this adjustment varies across different application
domains)” ‘Bhusan Explained’. Researchers and ASV system developers aim at
minimising these error rates. Lower the EER better is the ASV system
.
Security of Voice biometrics: a growing concern
One of the key problems with the usage of voice biometric application corresponds to the
growing concern about its security. With recent advancement in technology, there are
commercial applications (available online) capable of producing voices that sound as
natural as if spoken by a real human. For human ears it is very dif
fi
cult to detect if the
4. voice was created using computer algorithms. Therefore, fraudsters/attackers aim at
launching spoo
fi
ng attacks on voice biometrics in order to gain illegitimate access to
someone else’s voice biometrics (say, bank application with an aim to steal money).
However, researchers like Andrew Ng, Bhusan Chettri, Alexis Conneau, Edward Chang,
Demis Hassabis and more in the speech community have also been working hard towards
design and development of spoo
fi
ng countermeasures with an aim to safe-guard voice
biometrics from fraudulent access. The next article, follow up on this, would be explaining
more about spoo
fi
ng attacks in voice biometrics and mechanisms/algorithms used to
counter such attacks.
References
[1] D. A. Reynolds, “An overview of automatic speaker recognition technology,” 2002 IEEE
ICASSP, 2002, pp. IV-4072-IV-4075
.
[2] Bhusan Chettri. Voice biometric system security: Design and analysis of
countermeasures for replay attacks. Ph.D. thesis, Queen Mary University of London
.
[3] ORCID, DBLP
[4] Automatic Speaker Recognition and AI