What is voice biometrics?

Biometrics allows corroborating the identity of a user through the analysis of one o more?physical features?such as the face, voice, fingerprint, iris, vein pattern or?behaviour?such as signature, gait, and interaction with mobile applications.

Each of those features, also known as characteristics, has its peculiarities and, when chosen, must keep in mind the security and functionality of the system where they are integrated.

But what is voice biometrics?

Voice biometrics?is a technology that identifies and authenticates users by their voice. This technology considers that the human voice is unique, and each person has a distinctive frequency pattern and features in their voice.

This technology is simple: a voice shape is recorded and afterwards analysed to get a set of features to identify the person.?Rhythm, pitch, frequency, and timbre are some of the characteristics used in voice analysis.

Voice biometrics systems use?machine learning algorithms and voice sample databases. These algorithms analyse and compare the known voice features with those of unknown ones. If the match between the voices is enough, the system can confidently determine who is speaking.?

Proper training of the models with vast voice samples during registration is critical for getting a quality?voice biometrics template.?

It is said that voice biometrics can lead to doubts due to voice changes by illness, fatigue, or the passing of time. Nevertheless, the use of deep learning technology and the expertise of engineers can overcome any challenge and identify a user’s voice in different use cases.

The most common cases are identity verification in a security system and?user authentication in call centres, such as the success story of Santalucia.?

What is voice recognition?

Voice recognition is a technology that enables a computer or an electronic device to identify the pronunciation voice of a person. It has several uses. The most important are text input on mobile devices, automatic transcription of talks and meetings, smart home devices control and information services access by phone. It is also used for virtual assistance allowing the user to interact with voice systems through voice commands.?

Voice recognition is based on voice biometrics.?In other words, it is based on the measure and analysis of unique features of a human voice. Although they use similar technologies, they are not the same. They do not share the same goal. In a nutshell, voice biometrics distingue one person’s voice from another, focusing on verifying that individual’s identity. In contrast, voice recognition eases communication among users and digital devices.?

Differences between voice biometrics and voice recognition

Voice biometrics?use unique characteristics of a person’s voice to trustworthy identify them. It is used to authenticate a user in a system to protect individual privacy by ensuring only they can access sensitive information or carry out specific actions.

On the contrary,?voice recognition?turns speech into text to be processed with a computer or digital device. IA assistants like?Google Assistant, Alexa, or Siri?use voice recognition. These systems allow users to perform actions and obtain information using voice commands.?

In short, voice biometrics identify people, while voice recognition processes speech and turns it into text that a computer can understand.?

As mentioned,?voice biometrics systems are used in security environments.?The main applications relate to users’ identity verification when logging in, approving financial transactions, and accessing call centre services.?Contrary to voice biometrics, voice recognition is focused on personal assistance apps such as digital assistants and in the health industry?to speed the recording of medical information.

Advantages of voice biometrics

Biometrics technologies based on voice analysis have plenty of benefits and are widely used in many industries. Here are the main reasons for their usage:?

• Strength:?the speech signal is quasi-stationary when analysed at short time intervals. It allows extracting features with high discriminative power to differentiate among individuals.?
• Low intrusiveness:?unlike other modalities of authentication, which require user active collaboration or specific hardware, voice biometrics only need audio capture through a microphone.
• Availability:?recording a voice clip is considered a standard identification method. People are used to this kind of method. They are not deemed unusual. Furthermore, biometrics systems that analyse the human voice are easily integrated into call centres, voice assistants, and mobile devices.
• Secure voice authentication:?voice is a unique biometric feature that can be used to authenticate a person’s identity securely.?
• Easy to use:?it is only required to speak to a microphone for the system to recognise a voice.
• Accessibility:?it is beneficial for disabled people with writing problems or difficulties using tactile devices.?
• More convenience:??it enables users to perform tasks without typing or using touch devices.
• High efficiency:?it allows users to perform tasks faster and more efficiently, as there is no need to type up.
• Accuracy:?voice biometrics can be more accurate than writing or touch devices, preventing errors and increasing productivity.

A brief history of voice biometrics

Voice biometrics technology has significantly evolved in the last decades. Here are some of the most important milestones of its evolution:?

• In?the 1970s, researchers started?investigating?the possibility of using?voice as an authentication method?in this decade. Nevertheless, many technological limits stop the development of practical solutions.?
• In?the 1980s, the?first voice recognition systems?appeared. These systems used signal-processing techniques to identify users’ voice patterns. However, they were very rudimentary and had a high error rate.
• In?the 1990s, voice recognition systems became more accurate due to?advances in signal-processing techniques.?Furthermore, machine learning techniques started to be used to train voice algorithms.?
• In?the 2000s, expert companies developed?voice biometrics systems?that used numerous voice features, such as intonation, rhythm and speed, improving recognition?accuracy. They also began to use emotion analysis techniques to detect fraud.
• In?the 2010s, companies developed?voice biometrics systems?that ran in?real-time, making them ideal for mobile and online security applications.?

Voice biometrics technology keeps currently evolving, and companies are exploring new techniques to improve accuracy and efficacy. For instance, researchers are testing systems that detect illnesses through voice, such as Parkinson’s disease and depression. Furthermore, voice biometrics technology is expected to integrate with other authentication modalities. The integration with other biometrics, such as fingerprint or facial recognition, will enable to provision of multimodal authentication solutions that are highly secure.?

Voice biometrics use cases

The human voice is part of our daily. It is considered a mechanism that accesses services, apps and devices due to their ease of use. Some examples of their use are:?

• Identity verification: many security systems, like login systems in mobile devices or online services, use?voice technology to verify the identity of a user. These systems can compare a user’s voice with the voice input kept as a voice print and determine if the person who wants to access the system is the same that the reference sample.
• Travel safety:?some airports and mobility companies use voice biometrics to verify the identity of passengers and employees. It ensures that only authorised people get into planes, trains or buses.
• Emotions and mood analysis: this kind of technology is able to check individuals’ emotional states. As a result, technology can determine if a person is exhausted, upset, or full of joy.?
• Personal support: virtual assistants often use voice biometrics to recognise users and adapt user experience. That is to say that a digital assistant can use voice technology to know the user’s mood and adapt its answer and action to their mood.?

MobbID provides an immersive call experience for Santalucia’s customers

Voice biometrics is frictionless and?highly secure?and improves the user experience by simplifying the interaction processes between customers and agents in call centres.

The number of policyholders engaged with their insurance company is at most 19%, according to?the STIGA Customer Experience Rate.

Santalucía, a Spanish insurance company, exceeded this rate by incorporating voice biometrics into its call centre processes.?

The company found that one of the channels most used by its customers was the phone. Nevertheless, the customer service process needed to be optimised and fully adjusted to the needs of policyholders.?

The company implemented Mobbeel’s voice biometrics into its call centre service to optimise this.?Now its customers can identify themselves through their voice when calling the call centre by saying a?Spanish fixed phrase?previously registered in the system:?“En Santalucia, mi voz es mi contrase?a“.?

When the customer calls, they say this sentence and?the call is automatically passed on to an agent?who does not have to identify the user. Our solution frees agents from time waste placing customers and improves the customer experience by removing frustration associated with cumbersome identification processes.

As a result of implementing?Mobbeel’s voice biometrics technology,?Santalucia achieves a 39% satisfaction rate, becoming a leading company in customer satisfaction.

Operation of a voice biometrics system

A complete voice biometrics system must be able to verify a person’s identity?through their voice and guarantee that there are no attack attempts to hamper its operation mode.?

The?ISO/IEC 30107-11?define an architecture adopted by Mobbeel, providing the highest level of trustworthiness when verifying user identity.

There are essential elements for any voice biometrics system to work:

• A microphone:?to capture the human voice.?
• A signal processor:?to convert the audio signal into a digital form that a computer can process. The most critical part is the voice modelling resulting in an attributes vector of the voiceprint.?
• Voiceprint databases:?to compare the unknown voice with the known voiceprints stored in the databases.
• Decision-making and user interface:?to allow the user to interact with the system and give results after the decision-making about the user identity or carry out some actions once identified.?

When speaking to a voice-biometrics microphone, the signal processor captures and converts the audio signal into a digital form. After that, the signal processing algorithm compares the audio with the voice models stored in the database. The system can confidently determine who is speaking if there is enough match between the unknown and known voiceprints.?

Capture system

The first step within the identity verification biometrics process is to capture the physical or behaviour attribute object of analysis. In the voice biometrics process, an audio clip with a sentence pronounced by the user is recorded.

The system developed by Mobbeel offers high versatility being able to identify the speaker regardless of the language they speak and the type of phrase, whether fixed or free.?

It allows for implementation cases where the user is asked to pronounce a fixed text or identifies them automatically based on their natural speech.

Its design is also suitable for?multi-channel?use as it supports audio from the?telephone channel?(fixed, mobile and IP networks) and?high quality.?Output results are related to the medium used since some channels, such as conventional telephone networks, use filters that remove signal information with consequences in the accuracy rate.?

As part of the capture module, the technology includes some algorithms in charge of?assessing the quality of the input audio, determining if it meets the minimum conditions for performing biometrics operation. These checks analyse the audio quality (SNR?or signal-to-noise ratio) and minimum voice content by estimating?voice activity detection (VAD).

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