Google uses its own speech recognition/dictation resources as well as Google Translate to perform the transcription and translation operations. It is thought to use SVOX’s text-to-speech rendering software to recite the translated output, although its acquisition of Phonetic Arts leads me to believe that the search giant is getting ready to incorporate its own text-to-speech rendering (probably with a human-sounding voice that conveys emotion.

Does Google’s entry spell the end for competing products? Not necessarily. As Greg Sterling noted in a post on Internet2Go, the “free” Google Navigation application for Android phones barely made a dent in Telenav’s business. The take-away is that creativity and attention to a great user experience is rewarded in the marketplace. Google’s technologies for speech recognition, translation and synthesis will continue to be important resources for its own products and services, but other, product-oriented companies will continue to introduce services that successfully bond with customers, even if they compete directly with the technology that they are employing.

Indeed, Google has provided this resource for real-time translation of text for more than three years. It now supports over 40 languages. Yet there is not a machine-to-machine language expert who believes that such a service will ever be “100% accurate.” A consensus among those who left comments on the TimesOnline site believe that accuracy is still in the 50% range.

I, personally, believe that measuring the accuracy of a speech-to-speech translation resource is not a meaningful measure. Even if a system were able to recognize 9 out of 10 words dictated into a system, the one word that is misrecognized can often distort the meaning of the entire phrase. The problem can be compounded when that initial transcription is translated into another language for re-rendering through a text-to-speech engine.

That said, Google’s “can-do” attitude toward real time translation is laudable. It has access to an ever-growing database of multi-lingual search terms and search results. It is now adding spoken search and dictation terms emanating from the Google Mobile App on a multiplicity of smartphones. Based on an evaluation of simultaneous improvements in machine-aided speech recognition, transcription and translation, one can see why Google’ Ochs has had his confidence raised.

My point is that all such improvements are asymptotic. They approach 100% accuracy, but they will never get there. This is why the concept of “satisficing” is growing in importance. Google has taken its time-tested approach both to the underlying technological challenges and to the roll-out of new services. It’s technological approach is pure statistics. It captures, stores and processes a huge amount of utterances. It does it over-and-over again. It may not get them all right, but the result is constant improvement and, starting about four months ago, was deemed “satisfactory”.

As for “sufficient”, that is the end-users’ call, and the Google’s roll-out strategy, which often confuses people about whether a service is “in the lab”, “in beta” or “generally available” is better understood as a test of sufficiency. Google knows a service is sufficient when it’s activity logs show that people are using it. That is satisficing in action. It’s not optimal, but it is effective. Google is, in effect, doing the market conditioning and expectation setting for solutions providers that already includes IBM, Nuance, Cisco, Loquendo and other speech processing specialists. But, given that it is a network service, it is also staking out new ground potentially for incumbent network operators to avoid becoming “fat-dumb pipes” and for cloud computing specialists to expand their global reach.

One of the early fruits of the partnership is an application for the Apple iPhone and RIM Blackberry that performs real time translation between spoken English and Arabic. This YouTube video provides a demonstration. Mobile translation of this sort is something akin to the Holy Grail of natural language voice processing. Offering the function as a Smartphone app is a real coup. It benefits from nearly twenty years invested by Sakhr in building a knowledge base of Arabic phrases to support optical character recognition (OCR), machine translation (MT), data mining and search.

Sakhr’s long-standing expertise in the field attracted the attention of BBN Technologies, which incorporated the technology into its work on the U.S. Defense Advanced Research Projects Agency’s (DARPA’s) GALE Project (referring to “Global Autonomous Language Exploitation” efforts to capture and recognize huge amounts of spoken words and text in a variety of languages. IBM and SRI have been vying with BBN over the past few years to make breakthrough advances in “natural language understanding” across a multitude of languages, dialects and modalities.

The mobile application is not related to GALE, but it is an impressive demonstration of Sakhr’s underlying technology. It is the product of a partnership between Dial Directions and Sakhr that dates back to 2008 when the two companies formed a developmental partnership to address “language application technology for mobile, cloud-computing environments.” The result was a “first of its kind open speech-to-speech mobile translation application for the U.S. government and business customers.”

The video demonstration is, indeed, impressive. The challenges surrounding transcription (even in a single language) are formidable. As the company brings the application to the marketplace it will need to manage user expectations surrounding “accuracy” – which is a very tough nut to crack in the transcription world where mis-recognizing a single word can distort the meaning of an entire utterance. In the commercial marketplace, that is the proverbial, technological elephant in the room.