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05.24.07
Experiment with GOOG-411
 By
Chris Smith
Google recently released their new Voice Local Search in beta, also known as “GOOG-411?, and I thought I’d kick the tires a bit. They also mentioned it on their new Google Lat Long Blog.
I used to work for a major telco (Verizon), and we had a number of researchers working on various voice recognition systems. I’ve also tinkered a bit with applications using text-to-speech voice synthesis software such as the AT&T Labs Text-to-Speech software, so I’m familiar with some of the issues that are commonly associated with these types of systems.
I was curious about whether Google’s clever engineers had perhaps improved upon some of the issues involved with having software recognize spoken words, and I also wondered how the quality was of their voice-synthesis.
I’ve seen lots of problems with such systems. Back when I worked for Verizon, many of our larger facilities had automated phone directories so that you could call into a central number, state the name of the individual you were seeking, and the system could automatically connect you. For simple names and straightforward caller voices, these systems worked pretty well. But, I witnessed a number of occasions when it worked frustratingly. For instance, one of the Russian-American technical directors I worked with retained a heavy accent from his home country, and the voice-recognition system constantly misunderstood him, despite the fact that the systems supposedly were built with heuristics so that they were supposed to auto-correct and improve over time. Didn’t happen.
Further, while I have no real accent at all, times when I would try to reach someone with an unusual name such as a Chinese coworker were equally frustrating. It wouldn’t matter if I used a Chinese pronunciation, nor American phonetic pronunciation — for some names the system simply wouldn’t work.
I’m also very sympathetic to disabled people for whom automated systems often offer the greatest hope for improved quality-of-life, but also often produce the greatest let-downs. My father, a dynamic and clever scientist in his heyday was eventually beaten down some by various diseases, including a stroke late in his life. The stroke took away most of the use of the left side of his body — arm and leg, particularly. After he had retired, he enjoyed using his computers a lot, but the loss of the use of one of his hands made every task far more time-consuming. His solution for this was the purchase of some voice-synthesis software, but the functionality was never all that great because his speech had been slurred some by the stroke. So, he often had to repeat commands, and correct stuff with his good hand. When he called into company service centers, the situation always seemed a bit torturous to me as well. His attempts to vocally navigate call trees were often error-prone, resulting in an even more frustrating process than the usual, never-ending maze of call trees.
Good programming is often about handling of all the exception cases and extremes that may impact any given system. Speech and understandability are complex problems, and they’re areas where fuzzy logic and adjusting recognition template tolerances may never work perfectly for all people (until or if we also can match up AI to the problem). Even so, the criteria of “how well does it function for extreme cases” should be applied to assess the quality of the system, and one would hope that it wouldn’t be easy to find places where the system fails.
To do this quick assessment, I first performed searches for stuff in Google Maps to see what results they have in their database. Google Voice Local Search is basically a voice recognition and voice synthesis interface that have been put on top of their regular Google Maps search engine and results. So, I wanted to compare the vocal search results with the browser search results.
Continue reading this article.
About the Author:
Chris formerly headed up the Advanced Technology Department for Verizon Superpages.com (later spun off under Idearc Media), where he worked for ten years, specializing in patent-pending work in mapping, local search, analytics, and SEO. As the natural search optimization expert for Idearc, he founded and chaired the company's SEO Council. Chris is currently a Lead Search Strategist for Netconcepts, a search optimization firm. Chris is a regular columnist for Search Engine Land, covering the 'local search' beat. He also blogs for Natural Search Blog, and speaks at industry conferences such as Search Engine Strategies.
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