Glossary of Key Terms

Biometrics refers to the measurement of a physical feature or repeatable action of an individual. There are many forms of biometric measurements. Examples are fingerprints, retinal/iris scans, facial recognition, hand geometry, signatures, DNA, and of course voiceprints. Voice biometrics is the measurement of how a person speaks.

Voice biometrics is the measurement of how a person speaks. Voice biometric systems use the measurement of spoken voice to distinguish between people, usually to validate or invalidate a claimed identity or determine identity from a group of people.

Classification is the process for categorizing a person by evaluating voice samples from that person. Examples include male or female, young or old, or emotional state. Classification systems can be used within Identification systems to help reduce the number of comparisons necessary and to increase confidence in scoring. For example, if the person’s gender is clearly male, then it is not necessary to compare against voiceprints that are known to be female.

EER, or Equal Error Rate, is the operating point where the percentage of false acceptances is equal to the percentage of false rejections. The lower the EER value, the better, as it is desireable to be both very good at recognizing valid system users as well as very good at screening out imposters and fraudsters. This is the most common term used to judge the accuracy of biometric and other security systems. Note that results can be misleading if EERs are based on finite laboratory datasets. Thus, VBG only uses randomized real-world data when providing EERs.

FAR, or False Acceptance Rate, is the percentage of times when the system will incorrectly let an imposter or fraudster in as a valid user. This scenario is sometimes also referred to as a 'Type II' error. Giving unauthorized users access to any system can have profound implications, so it is very important to tune biometric systems to low FAR levels.

FRR, or False Rejection Rate, is the percentage of times when the system will incorrectly reject a valid user. This scenario is sometimes also referred to as a 'Type I' error. Rejecting a valid user is an inconvenience and this can have implications for long-term user acceptance. To help manage these types of errors, tuning is recommended, along with offering retries.

Speech samples submitted to the voice biometric engine are evaluated, and then unique vocal characteristics are modeled and extracted using sophisticated audio signal processing techniques. The process that performs these tasks is commonly referred to as 'feature extraction'.

Interactive Voice Response refers to technology in which a person is able to interact with a computer application through a telephone call. The person either uses the telephone keypad or speaks to provide input. The computer application issues commands that cause recorded or synthesized computer voice to play back to the person.

The use of voice biometric authentication in conjunction with IVR systems is a common practice these days. IVR systems provide a convenient means to capture voice samples. Furthermore, IVR systems are often the entry point for contacting a business over the phone, making the IVR system an ideal place to automate the authentication process.

One of the potential weaknesses of a biometric system is the susceptibility to a recorded occurence of the biometric being used. For example, for facial biometrics, a fraudster may attempt to use a picture of the correct person to trick the system. Therefore, many biometric systems perform "liveness testing" in order to validate that a biometric sample is from a live person.

For voice, companies use different techniques, such as listening for exactly the same audio as what was submitted in an earlier transaction or identifying noise from recording devices. For applications that are at risk to recorded playback attacks, VBG recommends using RandomPIN™, which inherently includes liveness testing in the process.

More recently, VBG researchers have been developing sophisticated anti-spoofing algorithms using advanced deep learning techniques. We are now able to more accurately determine if a submitted speech sample is a recording, or is synthetic speech.

An authentication factor refers to a piece of information and/or a technique used to authenticate a person’s identity. Factors are something you have, such as a picture ID or token; something you know, such as password, PIN, or answer to a shared secret; or something you are, such as a biometric in the form of a fingerprint, voiceprint, etc.

Multi-Factor Authentication, or MFA, is a system where multiple factors are obtained during a single session to authenticate an individual, resulting in greater confidence in the authentication. VBG strongly encourages the use of MFA whenever possible.

VBG NaturalSpeech™ is a passive use case supported by VBG where a voiceprint is created from one or more speech samples of unknown content. In order to capture enough phonetic diversity in a person’s voiceprint, the voice samples must be of sufficient duration and content. For best results, typially 45-60 seconds of speech are needed. However, when verifying against a VBG NaturalSpeech™ voiceprint, only 2 seconds of speech are needed.

Voice biometric systems rely on analyzing voice samples. A Playback Attack is when an impostor uses a recording of a person's voice during a verification event in order to fraudulently be "passed" by the voice biometric system.

RandomPIN™ is an active use case supported by VBG where a person enrolls by speaking several sets of digit strings. To verify, the VBG system generates a random number, typically four or five digits. Because the verification phrase changes every time, RandomPIN™ is less susceptible to recorded playback attacks compared to fixed passphrases. And, the use of random numbers is very convenient -- there is nothing for users to remember.

A text-dependent voice biometric interaction means that the content of what the person is speaking is essential to the biometric analysis. The use of text-dependent prompting can help to minimize the time needed for enrollment, as well as simplify the process for end users.

Both of VBG's active use cases, Static Passphrase and RandomPIN™, are text-dependent.

A text-independent voice biometric interaction means that the content of what the person speaks is not relevant to the biometric analysis. That is, the voice biometric system is able to analyze the voice regardless of what the person is saying. Enrolling a person’s voice with no constraints on the content means that more speech is typically necessary to capture the unique characteristics of a person’s voice. VBG gets good results when creating voiceprints with 30-45+ seconds of speech.

Our passive use case, VBG NaturalSpeech™, is a good example of a text-independent method. We use this technique to process post-call recordings, or actively authenticate a caller in real-time as they are speaking with an agent.