In response to incentives offered by the U.S. government and building on concepts from linguistics and mathematics, a group of computer scientists at Carnegie Mellon University designed speech recognition systems that achieved a vocabulary of one thousand words.
Although speech synthesis was achieved as early as the mid-1930’s by researchers at the American Telephone and Telegraph Company’s (AT&T) Bell Telephone Laboratories, the more difficult task of getting machines to recognize speech was not accomplished until much later. The early achievements in mechanical speech synthesis at Bell Labs
In 1952, scientists at Bell Labs created the first system that could recognize speech, but it was limited to the numbers one through ten, spoken in English. Although the recognition of ten separated words spoken by the same speaker was far from the goal of recognizing complete sentences, it was believed that the system could be useful in certain contexts in which users needed to have their hands free and at the same time needed to communicate numbers. Almost two decades later, a commercial product with only slightly more advanced capabilities appeared, the VIP 100, made by Threshold Technology.
Over time, it became obvious that solutions to speech recognition would require expertise from several disciplines, including mathematics, computer science, and linguistics as well as acoustics. A basic mechanical problem was the lack of sufficiently powerful computer hardware to be able to capture and store the acoustic data, access the data, and perform the analytic calculations to match the data with human speech patterns. Other challenges were linguistic, such as the issue of homonyms—that is, words that sound the same but have different meanings that can be determined only by context. Even though some limited success had been achieved with having a speaker pause between spoken numbers and having a limited number of speakers, researchers were faced with problems stemming from the lack of segmentation in normal speech and the tremendous amount of variability in tonal qualities and habits of pronunciation.
During the 1960’s, linguists, led by Noam Chomsky,
One of the early centers for the formal study of artificial intelligence
The first few years of this work were supported by ARPA, the Advanced Research Projects Agency
In response to the ARPA goals, Reddy and his graduate students at Carnegie Mellon University
By 1976, as the government’s five-year deadline approached, the Carnegie Mellon group had developed three systems, each programmed with different kinds of searching structures that had various advantages and disadvantages, but all of which contributed to subsequent developments in the field. The Hearsay II system was designed for very large vocabularies and higher levels of interpretation, but it took a long time to process the data.
The Dragon system,
The system that came closest to meeting the ARPA project’s goals was the Harpy system,
Outside Carnegie Mellon University, only one other competing team was able to complete a viable system: the Hear What I Mean (HWIM) system
Although the government’s evaluators agreed that CMU’s systems had achieved some of ARPA’s goals, including the one-thousand-word vocabulary, some within ARPA had doubts about the short-term feasibility of practical applications, and funding ceased for a few years. Important developments were occurring in related fields, however. In the same year that ARPA evaluated the speech recognition systems, Ray Kurzweil announced an important achievement in a related aspect of human-computer interactions: a computer-based system that could read printed text to the blind. Like Reddy, Kurzweil was also interested in optical pattern recognition and artificial intelligence.
After the ARPA project ended, James Baker, who had developed the Dragon system at CMU, continued his work on speech recognition while working for IBM and other organizations, in collaboration with his wife, Janet Baker,
Carnegie Mellon University continued its research programs for speech recognition in the 1980’s, and government funding was restored. New heights of accuracy and speed were achieved, especially by the Sphinx system,
In subsequent decades, mechanical voice recognition branched into many high-pressure fields that rely on specialized vocabulary, including medicine, law, automated phone answering systems, and stock trading. Consumers gradually became more familiar and comfortable with the experience of having their spoken words recognized by computers.
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Baig, Edward C. “Teaching Personal Computers to Listen.” Fortune, December, 1985, 121. Describes a thousand-word-capable consumer system introduced by IBM in 1985. This system was based on one of the CMU prototypes. -
Jurafsky, Daniel, and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Upper Saddle River, N.J.: Prentice Hall, 2000. Comprehensive college-level textbook is organized into four main sections covering words, syntax, semantics, and pragmatics. Includes diagrams, charts, and tables. -
Robinson, Arthur L. “Communicating with Computers by Voice.” Science 203 (February, 1979): 734-736. Provides clear comparative description of the seminal speech recognition projects funded by ARPA, including brief technical explanations of each. -
Weinschenk, Susan, and Dean T. Barker. Designing Effective Speech Interfaces. New York: John Wiley & Sons, 2000. Focuses on the steps to take in project design and on human factors rather than on engineering and mathematics. Includes tables, glossary, bibliography, and index.
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