The program serves as an antidote to the reticence that IT and customer care professionals have shown (especially during the economic downturn) toward launching the lengthy design, deployment and debugging processes associated with natural language understanding (NLU) and enhanced call processing. The pitch is quite simple. Nuance first shows that callers benefit from shorter wait times and fewer call misdirections when a well-designed Call Steering app is deployed. Then they demonstrate how companies can benefit from shorter wait times and greater cost certainty when they adhere to a fixed scope deployment approach.

The term “fixed scope” is well chosen. Based on years of experience with Call Steering implementations among large global companies across multiple vertical industries, Nuance understands the application characteristics and communications architectures that will benefit from Fast Start. It has distilled those learnings into a “configuration file” which provides a foundation and design framework for getting started. Customers and clients are not being offered a “crippled app” or proof-of-concept, instead they are deploying the same code that has worked in full scale deployments.

The constraints have to do with the demographics and architecture of targeted companies. Based on experience in the field, Nuance recognizes that Fast Start will work most effectively for firms limited to 80-100 call destinations and 5-10 instances that require serious interpretation of “disambiguation” of a caller’s intent. In those cases, implementations can be completed in as few as 90 days and the total start up costs are a significant discount from the “full service” approach.

The offer seems very attractive to healthcare providers and utilities where companies have high standards for customer satisfaction but often lack the call volumes that justify investment in NLU. As Foley explained, Nuance had observed a threshold in the 10-12 million calls per year range (with some of the largest systems handling over 100 million calls each year). Fast Start can make Call Steering attractive when annual call volumes are less than 4 million. The clear message coming from Nuance is that “Natural Language Call Steering” is not too expensive for mid-sized businesses.

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.