What is Voice Biometrics?

Many personal and commercial security solutions are using biometrics as an advanced layer. With your genetics and habits serving as unique identifiers, this may appear to be infallible.

 

For a brief biometrics definition, biological measures — or physical features — that may be used to identify individuals are referred to as Biometrics. Fingerprint mapping, facial recognition, and retina scanning are all examples of biometric technology, although these are only the most well-known.

 

Let us now move forward, and understand more about Voice Biometrics.

 

 

What is Voice Biometrics?

 

To identify an individual or a group, biometric identification employs statistical approaches based on biological traits. Fingerprint, face, voice, and typing cadence are the most often utilized biometric traits for authentication (the speed at which you type).

 

Consider Apple : TouchID and FaceID are both forms of biometric identification.

 

The subset of biometric identifiers that rely on voice is known as voice biometrics!

 

Voice biometrics, also known as voice verification or speaker identification, enables rapid, smooth, and secure authentications for applications ranging from websites to call centers, as well as mobile apps and voice assistance.

 

The distinctive features of a speaker's speech are used in voice biometrics to validate their identity (authentication) or identify them from a collection of known speakers (identification).

 

These distinguishing traits are a result of physical (anatomical facts such as the length of your vocal cord) and behavioral (language, dialect, and accent) qualities. These many qualities alter the frequencies and rate of change of frequencies produced by speakers when they pronounce certain words and phrases.

 

Also Read | AI in Music Industry

 

 

How does Voice Biometrics Work?

 

Voice Biometrics systems generate a template (or voiceprint) from one or more samples of a speaker's voice acquired at initial enrollment and utilize this for comparison during subsequent sessions for authentication or identification. 

 

In general, Voice Biometric systems are either text-dependent (also known as active), which require the speaker to say the same thing during authentication as during enrolment, or text-independent (also known as passive), which do not require the speaker to say the same phrase and are thus better suited to conversational interactions.

 

Since voice biometric systems are probabilistic, the findings are expressed as a likelihood that the present speaker is the previously enrolled speaker. Before applying the authentication or identification results in their business operations, organizations deploying these systems must select an acceptable degree of trust.

 

Voice biometrics authentication generates your voice signature from a recording of your voice and utilizes it to identify you afterwards. It depends on each system whether or not it relies on you stating a certain phrase; more on that below.

 

Here's how Voice biometrics works, which has been greatly enhanced by recent advances in AI:

 

• After your voiceprint has been developed, the system is ready to go and you can authenticate using your voice.

 

• The speech biometrics engine computes your voiceprint and compares it to the one connected to you in its database to recognise you. You're in if it does!

 

• Modern speech biometrics algorithms are sophisticated enough to operate with voiceprints that are not language-specific. In other words, these technologies are applicable to every language and any country.

 

Also Read | A guide to Information Security

 

 

Categories of Voice Biometrics

 

Now that you're more comfortable with the fundamentals, let's look at the various families of voice biometrics.

 

Voice biometrics systems are classified into three types:

 

• Text-dependent active voice biometrics

 

• Text-independent active voice biometrics

 

• Text-independent passive voice biometrics

 

Let us understand more about what they mean. 

 

First, text-dependent methods ask to use a specific short phrase to recognise a speaker. 

 

For example, HSBC requires users to utter "My voice is my password" in order to access their phone support lines. This implies that you must expressly pronounce a certain phrase for the system to recognise you.

 

Text-independent systems, on the other hand, seek to identify speakers regardless of what they say. You don't have to say anything specific in this paradigm for the system to recognise you; you may say whatever you want. 

 

Now let is understand what distinguishes active and passive voice biometrics given that we understand the distinction between text-dependent and text-independent identification

 

In the event of active authentication, the system says something like, "To authenticate, please say "My voice is my password" (text-dependent) or "To authenticate, please speak anything" (text-independent). This is utilized in Amazon contact centers, for example.

 

But, what exactly is passive voice biometrics? This is when the identifying system recognises you without you needing to do anything special. As a result, because we don't want to force anything on the speaker, passive voice biometrics is mainly text-independent.

 

Also Read | How Spotify uses Machine Learning

 

 

Applications of Voice Biometrics

 

Voice Biometrics can be applied in various domains some of them are as follows:-

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Applications of Voice Biometrics

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1. Voice Authentication

 

When it comes to speech biometrics applications, the first thing that springs to mind is voice authentication, sometimes known as authenticating into an account using your voice.

 

This programme transforms the authentication process by eliminating the need for knowledge ("Do you remember your password?") or access to a device ("Do you have your phone where we just sent your verification code?").

 

Instead, it all depends on you being yourself, which makes the whole thing go a lot more smoothly.

 

Voice authentication is presently used in a variety of applications, including:

 

• Authentication on online websites and apps;

 

• Telephone and online transactions;

 

• IVR (Interactive Voice Responder) -based banking and healthcare systems

 

• Audio signatures to digital documents.

 

Voice authentication would be ideal for providing clients with a complete online voice shopping experience. With a Vocads campaign, walk customers through your business and then verify them with their voice to validate the transaction.

 

2. Crime Investigation

 

The use of voice identification is not limited to authentication. Consider a criminal case in which the police have an audio recording of a suspect they want to identify.

 

Using speech biometrics, they can trace the suspect's identity and take important steps toward solving the crime. What if none of the suspects match? Can voice signals still be useful?

 

If no specific individual fits the tape, detectives can still receive an approximation of the person's demographics, such as age, gender, and origin, which are all valuable clues for prospective high-stakes cases.

 

The use of voice biometrics made a significant impact in the Rebecca Zahau murder case. Her death was initially ruled a suicide in 2011, but it was reclassified as a homicide in 2016 when voice biometrics were used to demonstrate that "her" 911 call was not made by her.

 

Also Read | Role of AI in Criminal Justice

 

3. Zoology

 

On a lighter side, speaking of identifying individuals based on the sounds they produce, a group of international researchers used voice biometrics on a pack of wolves in Yellowstone National Park and were able to tell which person the sound came from based on their "howl prints." isn't it amazing!  

 

Voice biometrics can also be used to also track animals on the basis of sounds produced by them and can be an interesting way in saving these animals from the predators and help them from getting extinct.  

 

4. Healthcare and Diagnostics

 

Lastly, several academics have lately concentrated on extracting voice-based illness indicators from massive patient databases using AI. The goal here is to extract as much information about a patient's health status as possible from an audio sample.

 

Doctors, for example, strive to answer queries such as, "How probable is this patient to have condition A based on this recording?" "How far along in the progression of illness B is this patient?" For instance there is a system that discovers asymptomatic covid cases from cellphone records.

Massive improvements in neural networks over the last 2-3 years have allowed for the creation of voice biometric algorithms that are quicker, more accurate, and can authenticate users with less speech. In fact, ID R&D is now capable of outperforming a 4-digit PIN in many usage scenarios.

 

Also Read | Speech Analytics

 

Voice biometrics, like other biometric modalities, provide considerable security benefits over authentication techniques based on something you know (such as a password or response to a "secret" question) or something you have (like your mobile phone). 

 

Voice biometrics also enhances the user experience by eliminating the irritation associated with time-consuming login processes and lost or stolen passwords.