How it works
The "twin problem" is one of the most difficult for a voice biometrics system to handle—and no wonder, as the physiology of the two speakers is almost identical. But for everyone else, the voice is unique, like fingerprints or snowflakes. The voice is produced by a delicate combination of hundreds of features ranging from the length of the vocal tract to the shape of the vocal cords to the size of the sinus and mouth cavities. The tiniest variation in these details gives each voice a particular sound that is then combined with learned speech habits and vocal tics to generate speech.
Differences between genders, thought by many to be simply a matter of pitch, are actually a matter of physiology; the female vocal tract is 2cm shorter on average than the male. Humans can actually pick out these differences to identify the gender of speakers without seeing them, even if both speak at the same pitch—a pretty useful skill, when you stop and think about it.
Voice biometrics creates its "voiceprints" by analyzing the minute differences between voices. One system, from the UK-based Voice Vault, claims to make more than 100 separate measurements of each human voice. These kinds of measurements mean that voice biometrics goes far beyond simply comparing voice files to look for matches. Because it "understands" how a person's voice sounds, a biometric system can identify individuals regardless of what words they are saying. This "understanding" is then compacted into a set of data points called a voiceprint that can take up as little as a few kilobytes of storage space.
This means that voice biometrics systems can be "text-independent"; that is, they verify voice characteristics only, not attempting to understand what was said. But voice biometrics make a natural fit with speech recognition, and this pairing is termed "text-dependent." What a person says and how he says it are both measured in text-dependent systems, providing two-factor authentication from a single speech fragment.
Two ways to fail
Voice verification can fail in two different ways: by clearing impostors to proceed (false accepts) and by denying legitimate users access (false rejects). As the voice software is tuned, the two parameters move: tightening the algorithm decreases false accepts, but frustrates a higher number of legitimate users. Loosening the algorithm would make more users happy, but even a single imposter who gets through can cost a bank plenty of money.
If the false reject rate and the false accept rate are graphed on a chart, the point at which they intersect is called the "equal error rate," or EER. It's the point at which the values of both parameters are equal, and it's widely used in the industry as a metric for gauging particular voice biometrics systems. In a perfect world, the EER would be zero; in the world we've got, it can vary from less than one percent to more than 10 percent under bad conditions.
Current voice biometric technology is robust, and has few problems when presented with clean samples. Unfortunately, the prime delivery mechanism for voice samples is the telephone network, and telephones are notorious for not delivering clear signals. Even high quality wired phones simply lop off portions of the frequency spectrum, but the problem gets far worse when mobile phones are taken into account. Bad connections are always an issue, of course, but phones also use digital compression on voices. The end result can be a low-quality sample, and the problems only get worse when background noise is thrown into the mix.
It's a tough problem, but research into voice biometrics has been going on for decades in the lab. David Nahamoo, the CTO for speech at IBM Research, said that his company has been looking into it for nearly 50 years. IBM's research began in the 1960s and continued throughout the 1970s, but was halted in the 80s because it simply wasn't ready. The technology was not advanced enough to deploy in any sort of production environment and research was shelved until the late 1990s, when the development of new algorithms and the advent of faster computers made voice biometrics more practical.
According to Nahamoo, IBM's tech is mature but is still undergoing a "hardening" process in field trials throughout the world. "We've had a lot more knocks on her door the last four or five months," he said, but acknowledged that IBM has seen few major rollouts of its system, which has an average EER of around three percent. But Nahamoo also cautioned against using EER as a measure of any system's success (good news for banks, where a three percent false accept rate could cost millions of dollars). The final error rate of a voice biometric system is generally reduced by several orders of magnitude after combining the voiceprint analysis with a second mode of identification (password, PIN number, etc.)
We spoke with other conference attendees from major US financial institutions who preferred to remain off the record, but they pointed out that in the banking industry, algorithms would not be tuned for the EER, but would be biased toward fewer false accepts. That may prove more frustrating to customers who resent having to repeat their credentials to an automated voice, but it only makes sense in an environment where so much money rests on proper identification.