Speech Recognition Through the Decades: How We Ended Up With Siri

Looking back on the development of speech recognition technology is like watching a child grow up, progressing from the baby-talk level of recognizing single syllables, to building a vocabulary of thousands of words, to answering questions with quick, witty replies, as Apple's supersmart virtual assistant Siri does.

Listening to Siri, with its slightly snarky sense of humor, made us wonder how far speech recognition has come over the years. Here's a look at the developments in past decades that have made it possible for people to control devices using only their voice.

1950s and 1960s: Baby Talk

The first speech recognition systems could understand only digits. (Given the complexity of human language, it makes sense that inventors and engineers first focused on numbers.) Bell Laboratories designed in 1952 the "Audrey" system, which recognized digits spoken by a single voice. Ten years later, IBM demonstrated at the 1962 World's Fair its "Shoebox" machine, which could understand 16 words spoken in English.

Labs in the United States, Japan, England, and the Soviet Union developed other hardware dedicated to recognizing spoken sounds, expanding speech recognition technology to support four vowels and nine consonants.

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.

1970s: Speech Recognition Takes Off

Speech Recognition Through the Decades: How We Ended Up With Siri
Speech recognition technology made major strides in the 1970s, thanks to interest and funding from the U.S. Department of Defense. 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. Harpy could understand 1011 words, approximately the vocabulary of an average three-year-old.

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. (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.)

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.

1980s: Speech Recognition Turns Toward Prediction

Over the next decade, thanks to new approaches to understanding what people say, speech recognition vocabulary jumped from about a few hundred words to several thousand words, and had the potential to recognize an unlimited number of words. One major reason was a new statistical method known as the hidden Markov model.

Rather than simply using templates for words and looking for sound patterns, HMM considered the probability of unknown sounds' being words. This foundation would be in place for the next two decades (see Automatic Speech Recognition—A Brief History of the Technology Development by B.H. Juang and Lawrence R. Rabiner).

Equipped with this expanded vocabulary, speech recognition started to work its way into commercial applications for business and specialized industry (for instance, medical use). It even entered the home, in the form of Worlds of Wonder's Julie doll (1987), which children could train to respond to their voice. ("Finally, the doll that understands you.")

See how well Julie could speak:

However, whether speech recognition software at the time could recognize 1000 words, as the 1985 Kurzweil text-to-speech program did, or whether it could support a 5000-word vocabulary, as IBM's system did, a significant hurdle remained: These programs took discrete dictation, so you had … to … pause … after … each … and … every … word.

Next page: Speech recognition for the masses, and the future of speech recognition

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