In 2012, Amit Singhal, a senior executive at Google, predicted that within five years, his company would develop a conversational Star Trek–like computer, complete with natural language processing (NLP) and artificial intelligence. In the heat of competition, several other companies made similar bold predictions.
At the halfway point of that five-year period, natural language speech interfaces have come a long way. Advances in the technology have changed how we use our smartphones (Siri), our Web browsers and search engines (Google), and our cars (Ford Sync). Doctors are even using natural language processing to help record and transcribe information related to patients’ visits or medicines.
And today, "natural language understanding is core to intelligent assistance," says Dan Miller, founder and senior analyst at Opus Research, noting that "providing consistent, relevant, and personalized responses to customer queries through IVRs, chat, text, and social networks is a crucial part of large companies’ self-service strategies."
Miller expects to see more intelligent virtual assistants endowed with natural language capabilities. "Nuance's Nina, Apple's Siri, Microsoft's Cortana, and IBM's Watson are the most conspicuous, but there will be thousands of branded intelligent virtual assistants that can carry on conversations with individuals, making them vital aids for everyday activity and simplifying [consumers'] digital lives," he says.
Another use case cited by Miller is in the area of language translation, with semantic understanding engines, such as Linguasys and others like it, able to recognize topics and promote understanding in multiple languages. "Perhaps the most conspicuous use of this function was demonstrated by Microsoft recently as it did near-real-time translation of words spoken in one language into another in the course of a teleconference," he says.
But despite great progress, natural language processing's potential has not yet been fully realized, and the technology still struggles to climb the peak of inflated expectations. The Star Trek ideal is still something from science fiction rather than fact.
The good news, though, is that the challenges are no longer technical.
SPEECH ISN'T THE PROBLEM
"Speech recognition accuracy has gotten very good," says Ron Kaplan, a vice president at Nuance Communications and a distinguished scientist in Nuance’s Lab for Natural Language Understanding and Artificial Intelligence. "It's no longer an obstacle."
Today users aren't surprised when the speech recognition does what it's supposed to, but rather, when it doesn't, Kaplan states. "People expect the speech part to work, and it usually does," he says.
"Natural language understanding continues to benefit from improved speech recognition," Miller adds. "There is a symbiotic relationship between accurate rendering of spoken words and high-quality speech recognition for command and control, Q&A, and customer self-service."
Walt Tetschner, an independent speech industry analyst and consultant and editor of ASRNews, agrees. "I've had great successes [with natural language]. It can be done effectively. It works well," he says.
Natural language, Tetschner says, "offers an interface that is identical to speaking with a human. If the caller thinks that they are talking with a human, then it is natural language."
Perhaps natural language processing's greatest potential, though, lies in the contact center. FedEx is already using it in its call center to help customers with everyday tasks, such as scheduling pickups, tracking packages, finding the nearest FedEx location, getting rates, and ordering supplies. Turkcell, a wireless services provider in Turkey, is using natural language processing to handle 100 million calls a year from customers looking to complete a variety of transactions, from account inquiries to service changes.
These deployments, though, are few and far between. Most contact centers still are not using natural language processing.
Last year, Software Advice, a Gartner company that assists organizations in finding the products that best fit their needs, called the IVR systems of 50 Fortune 500 companies with business models that focus on customer service. Only two prompted the caller for an open-ended natural language response instead of offering a menu.
Tetschner notes that across vertical market segments, speech is included in only 23.1 percent of automated call steering applications. The vast majority of those with a speech interface use directed dialogue, not natural language.
Out of 1,202 corporate auto-attendants tested by Tetschner, fewer than 100 (8 percent) had a speech interface. Of those with speech capabilities, only four had natural language interfaces.
"When it comes to true natural language, there just aren't many implementations out there," Tetschner says.
"Many call centers have been slow to adopt the technology beyond trials," says Bill Meisel, president of TMA Associates and executive director of the Applied Voice Input/Output Society (AVIOS).
In reality, the majority of speech applications in use today are of the directed-dialogue variety. When combined with well-designed prompts and call flows, the higher accuracy of directed dialogue applications can offer a superior user experience, many industry experts conclude.