Speaker Recognition: Fundamentals and Applications

By Fouad Sabry

What Is Speaker Recognition

The identification of a person based on the features of their voice is referred to as "speaker recognition." The purpose of this information is to provide an answer to the query "Who is speaking?" Speech recognition and speaker recognition are both included in the broader concept of voice recognition. Verification of a speaker is distinct from identification of a speaker, and recognition of a speaker is not the same as diarization of a speaker.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Speaker recognition

Chapter 2: Speech recognition

Chapter 3: Voice analysis

Chapter 4: Authentication

Chapter 5: Interactive voice response

Chapter 6: Biometrics

Chapter 7: Electronic authentication

Chapter 8: Multi-factor authentication

Chapter 9: BioAPI

Chapter 10: PerSay

(II) Answering the public top questions about speaker recognition.

(III) Real world examples for the usage of speaker recognition in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of speaker recognition' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of speaker recognition.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 6, 2023

Book preview

Chapter 1: Speaker recognition

The identification of a person based on the features of their speech is referred to as speaker recognition. may apply to either speech recognition or recognition of the speaker. Identification of a speaker is in contrast to speaker verification, which is sometimes known as speaker authenticity. Speaker recognition is distinct from speaker diarization (recognizing when the same speaker is speaking).

The process of simplifying the task of translating speech in systems that have been trained on specific voices or the process of authenticating or verifying the identity of a speaker as part of a security process can be accomplished by recognizing the speaker. This can be done in systems that have been trained on specific voices. As of 2019, the process of speaker identification may trace its roots back around four decades and makes use of the acoustic characteristics of speech that have been discovered to vary depending on the person. These audio patterns are a reflection of both the anatomy and the behavioral patterns that have been learnt.

There are two primary uses for the technology and procedures that are involved in speaker recognition. The process of using a person's voice to verify their identification is known as verification or authentication. This occurs when a person makes a claim about their identity and the voice is used to back up that claim. On the other hand, identification is the process of attempting to establish the identify of an unidentified speaker. In a sense, speaker verification is a 1:1 match in which one speaker's voice is matched to a specific template, while speaker identification is a 1:N match in which the voice is checked against a number of different templates.

Identification and verification are two distinct processes with regards to safety and security. In order to function as a gatekeeper and allow users access to a protected system, speaker verification is often used. These systems function with the users' awareness and often demand their participation in order to function well. It is also possible to implement speaker identification systems in a covert manner, without the knowledge of the user, in order to identify talkers during a discussion, alert automated systems of speaker changes, check to see if a user is already enrolled in a system, and perform a variety of other tasks.

In forensic applications, it is typical practice to begin by carrying out a speaker identification procedure in order to generate a list of best matches, and then to carry out a series of verification operations in order to establish a match that can be considered definitive. By comparing the samples from the speaker with the list of best matches, one may determine whether or not they are the same person by analyzing the number of similarities and differences between the two. This is used as evidence by both the prosecution and the defense to establish whether or not the suspect is in fact the criminal.

In 1987, Worlds of Wonder released a doll called Julie that had one of the first examples of a commercially available training technology. At that time, the independence of the speaker was envisioned as a potential breakthrough, and the systems needed a period of training. Although it was characterized as a product which children could teach to react to their speech, a 1987 advertisement for the doll contained the slogan Finally, the doll that understands you. Despite this, the doll was marketed with the phrase Finally, the doll that understands you.

Enrollment and verification are the two stages that are included in any speaker recognition system. During the registration process, the speaker's speech is captured, and a variety of distinguishing characteristics are often retrieved to create a voice print, template, or model. During the verification step, a speech sample, also known as a utterance, is compared to a voice print that was produced in an earlier phase. In identification systems, an utterance is compared to many voice prints in order to find the best match(es), while verification systems compare an utterance to a single voice print in order to find the best match(es). Due to the steps required, verification may be completed far more quickly than identification.

Text-dependent and text-independent speaker recognition systems are the two main groups of these kinds of programs.

Text-Dependent:

Text-dependent recognition is the term used when it is necessary for the enrollment and verification processes to use the same text. Prompts in a system that is based on text may either be shared by all speakers (like a common pass phrase), or they can be individual to each speaker. In addition, the usage of knowledge-based information or shared secrets (such as passwords and PINs, for example) may be used in order to build a multi-factor authentication situation.

Text-Independent:

Text-independent systems are the ones that are used for speaker identification the vast majority of the time since they need the speaker's participation very little, if at all. In this particular instance, the text that is read during enrollment and the exam are distinct. In point of fact, the registration process could take place behind the user's back, as is the case with a large number of forensic programs. Since text-independent technologies are unable to match what the user stated during registration with what they say during verification, verification apps often make use of voice recognition in order to comprehend what the user is saying throughout the authentication process.

Techniques from the fields of acoustics and voice analysis are used in text-free information retrieval systems.

Pattern recognition is the key to solving the challenge of speaker recognition. Frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, vector quantization, and decision trees are some of the different technologies that are used to analyze and store voice prints. For the purpose of comparing utterances to voice prints, more fundamental approaches such as cosine similarity are generally used due to the ease with which they function and the accuracy with which they provide results. Cohort models and world models are two examples of the anti-speaker strategies that are used by some systems. The majority of the time, spectral characteristics are what are employed to indicate speaker characteristics. Speaker identification and voice verification are two applications that make use of a speech coding approach known as linear predictive coding (LPC).

Both the first and subsequent voice sample collecting processes might be hindered by excessive amounts of ambient noise. It is possible to use noise reduction techniques in order to enhance accuracy, but their application must be done carefully or it will have the reverse of the desired impact. Changes in the behavioral characteristics of the voice as well as enrollment performed on one telephone followed by verification performed on another may both lead to a decline in performance. Integration with goods requiring two different forms of authentication is likely to become more common. Changes in voice quality that come with advancing age may, over time, adversely affect system function. Although there is some controversy about the overall security effect that is imposed by automatic adaptation, some systems will adjust the speaker models after each successful verification in order to capture such long-term changes in the voice.

There has been a lot of discussion about the application of speaker recognition in the workplace as a result of the passing of laws such as the General Data Protection Regulation in the European Union and the California Consumer Privacy Act in the United States. Both of these pieces of legislation were introduced in their respective regions. In September of 2019, Irish voice recognition software firm Soapbox Labs issued a warning regarding the potential legal repercussions that may be involved.

The first application for an international patent was submitted in 1983. It originated from telecommunications research carried out in CSELT (Italy) by Michele Cavazza and Alberto Ciaramella as a basis for both the provision of future telco services to final customers and the enhancement of noise-reduction techniques across the network.

Speaker recognition technology was implemented at the Scobey–Coronach Border Crossing between 1996 and 1998, allowing enrolled local residents who had nothing to declare to cross the Canada–United States border after the inspection stations had closed for the night. This took place between 1996 and 1998. The software that was used was created by the voice recognition business Nuance, which is also the company that is responsible for developing Apple's Siri technology. Nuance purchased the company Loquendo in 2011, which was a spin-off from CSELT itself for speech technology. Callers