Challenges And Threats Of Automated Lip Reading- Part 1A

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Reading lips is a skill usually reserved for fictional spies or the hearing impaired, but researchers have spent years trying to gift the talent to computers, too.

A device capable of automated lip-reading would certainly be a game changer, raising questions of personal privacy while simultaneously creating new opportunities in the accessibility and security industries.

Credit MIT Technology Review

Don’t get too nervous (or excited) though, Ahmad Hassanat, a researcher at Mu’Tah University in Jordan, says we have a long way to go before machine eyes can tell what we’re saying.

As speech recognition technology improves, it’s natural to wonder whether computers will ever be able to lip read as well. Here’s a rundown of challenges involved.

Back in the 16th century, a Spanish Benedictine monk called Pietro Ponce pioneered the seemingly magical art of lip reading. Although the technique probably predates him, Ponce was the first successful lip reading teacher.

Then, as now, the technique was primarily used to help people with hearing difficulties interpret speech. But it is also used by others to eavesdrop on conversations.

Indeed, various experiments show that our ability to interpret speech improves when we can see the moving lips of the speaker. In other words, almost everybody uses lip reading to a certain extent.

That raises an interesting question. Can the process of lip reading be automated and performed by computer? And if so, how successful can this approach be and what kind of threat does it pose to privacy?

Today, we get some answers thanks to the work of Ahmad Hassanat at Mu’tah University in Jordan. He outlines the challenges that researchers face in the field of automated lip reading, otherwise known as visual speech recognition. What is clear from his analysis is that if lip reading is going to be successfully automated, significant challenges still need to be overcome.

The fundamental process of lip reading is to recognize a sequence of shapes formed by the mouth and then match it to a specific word or sequence of words.

There is a significant challenge here. During speech, the mouth forms between 10 and 14 different shapes, known as visemes. By contrast, speech contains around 50 individual sounds known as phonemes. So a single viseme can represent several different phonemes.

And therein lies the problem. A sequence of visemes cannot usually be associated with a unique word or sequence of words. Instead, a sequence of visemes can have several different solutions. The challenge for the lip reader is to choose the one that the speaker has used.

The problem is compounded by the fact that a speaker’s lips are often obscured so that on average, a lip reader only sees about 50 percent of the spoken words. The result is that lip reading is by no means perfect even for the most experienced practitioners.

Experiments show just how difficult it is, even when vocabulary is hugely limited. when people are asked to decide which of the digits 1 to 9 have been spoken, purely by lip reading, their success rate averages just over 50 percent. Not good at all.

So it is easy to imagine that the prospects for automating this technique are poor. But Hassanat points to a growing body of research that tackles this problem, aided by a rapid improvement in machine vision in recent years.

The first problem, for automated lip reading is face and lip recognition. This has improved in leaps and bounds in recent years. A more difficult challenge is in recognizing, extracting and categorizing the geometric features of the lips during speech.

This is done by measuring the height and width of the lips as well as other features such as the shape of the ellipse bounding the lips, the amount of teeth on view and the redness of the image, which determines the amount of tongue that is visible.

To Be Continued……………

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