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It draws what it knows about you to generate a contextual reply, and it responds to your voice input with personality. Like Google’s Voice Search, Siri relies on cloud-based processing. Remember how we started with 10 to 100 words, and then graduated to a few thousand? Google’s English Voice Search system now incorporates 230 billion words from actual user queries.Īnd now along comes Siri. The company also added Voice Search to its Chrome browser in mid-2011.

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In 2010, Google added “personalized recognition” to Voice Search on Android phones, so that the software could record users’ voice searches and produce a more accurate speech model. Google’s app adds, to its analysis, the data from billions of search queries, to better predict what you’re probably saying. In short, the bottleneck with speech recognition has always been the availability of data, and the ability to process it efficiently. Second, Google had the ability to offload the processing for its app to its cloud data centers, harnessing all that computing power to perform the large-scale data analysis necessary to make matches between the user’s words and the enormous number of human-speech examples it gathered.

First, cell phones and other mobile devices are ideal vehicles for speech recognition, as the desire to replace their tiny on-screen keyboards serves as an incentive to develop better, alternative input methods. The impact of Google’s app is significant for two reasons. Speech recognition technology development began to edge back into the forefront with one major event: the arrival of the Google Voice Search app for the iPhone.

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Windows Speech Recognition and OS X’s voice commands were interesting, but not as accurate or as easy to use as a plain old keyboard and mouse.

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Recognition systems did well when the language universe was limited–but they were still “guessing,” with the assistance of statistical models, among similar-sounding words, and the known language universe continued to grow as the Internet grew.ĭid you know speech recognition and voice commands were built into Windows Vista and Mac OS X? Many computer users weren’t aware that those features existed. 2000s: Speech Recognition Plateaus–Until Google Comes Alongīy 2001, computer speech recognition had topped out at 80 percent accuracy, and, near the end of the decade, the technology’s progress seemed to be stalled. VAL paved the way for all the inaccurate voice-activated menus that would plague callers for the next 15 years and beyond. The advent of the first voice portal, VAL from BellSouth, was in 1996 VAL was a dial-in interactive voice recognition system that was supposed to give you information based on what you said on the phone. However, you had to train the program for 45 minutes, and it was still expensive at $695. The application recognized continuous speech, so you could speak, well, naturally, at about 100 words per minute. Seven years later, the much-improved Dragon NaturallySpeaking arrived. In 1990, Dragon launched the first consumer speech recognition product, Dragon Dictate, for an incredible price of $9000. The ’70s also marked a few other important milestones in speech recognition technology, including the founding of the first commercial speech recognition company, Threshold Technology, as well as Bell Laboratories’ introduction of a system that could interpret multiple people’s voices. (The story of speech recognition is very much tied to advances in search methodology and technology, as Google’s entrance into speech recognition on mobile devices proved just a few years ago.) Harpy was significant because it introduced a more efficient search approach, called beam search, to “prove the finite-state network of possible sentences,” according to Readings in Speech Recognition by Alex Waibel and Kai-Fu Lee. Harpy could understand 1011 words, approximately the vocabulary of an average three-year-old. The DoD’s DARPA Speech Understanding Research (SUR) program, from 1971 to 1976, was one of the largest of its kind in the history of speech recognition, and among other things it was responsible for Carnegie Mellon’s “Harpy” speech-understanding system. Speech recognition technology made major strides in the 1970s, thanks to interest and funding from the U.S. They may not sound like much, but these first efforts were an impressive start, especially when you consider how primitive computers themselves were at the time.

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