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Frederick Jelinek

proper noun/YEL-in-ek/Also: Fred Jelinek

In plain wordsA Czech-born American engineer who led IBM's speech-recognition research and made statistics learned from data, rather than hand-written grammar rules, the basis of language technology.

  1. Czech-born American engineer who led the shift of speech recognition and language technology to statistical methods. After an MIT doctorate and a decade at Cornell University, he headed IBM's continuous speech recognition group (1972–1993), which treated recognition as decoding a noisy channel, combining hidden Markov modelshidden Markov model, n.: A model of a sequence, such as speech or DNA, in which hidden states — say, the words being spoken — change step by step at random, and each state gives off signals that can be observed, such as sounds. of acoustics with n-gramn-gram model, n.: An early type of language model that predicts the next word from just the few words before it, using counts of how often each word followed those words in a large collection of text. language modelslanguage model, n.: A system that has learned how likely different sequences of words are, so it can predict the next word in a text and, by doing that repeatedly, write text of its own. estimated from large text corpora and evaluated by perplexityperplexity, n.: A score for how surprised a language model is by real text; lower is better. A perplexity of 10 means guessing each next word is, on average, as hard for the model as guessing a ten-sided die's roll., a measure his group introduced (1977). Its methods became standard, and the same group produced the IBM models of statistical machine translationmachine translation, n.: Software that renders writing or speech in one language into another language, as online translation services and phone translation apps do. (early 1990s). From 1993 he directed the Center for Language and Speech Processing at Johns Hopkins University. A remark attributed to him in several versions — that every time he fired a linguist, his recognizer's performance went up — became a byword for data-driven over rule-based approaches. He received the Lifetime Achievement Award of the Association for Computational LinguisticsAssociation for Computational Linguistics, prop. n.: The scientific society at the center of research on language technology, from translation software to chatbots. It runs the field's leading conferences and a free online archive of their papers. (2009).

Related

See also
  • language modellanguage model, n.: A system that has learned how likely different sequences of words are, so it can predict the next word in a text and, by doing that repeatedly, write text of its own.
  • n-gram modeln-gram model, n.: An early type of language model that predicts the next word from just the few words before it, using counts of how often each word followed those words in a large collection of text.
  • hidden Markov modelhidden Markov model, n.: A model of a sequence, such as speech or DNA, in which hidden states — say, the words being spoken — change step by step at random, and each state gives off signals that can be observed, such as sounds.
  • perplexityperplexity, n.: A score for how surprised a language model is by real text; lower is better. A perplexity of 10 means guessing each next word is, on average, as hard for the model as guessing a ten-sided die's roll.
  • speech recognitionspeech recognition, n.: Technology that turns spoken words into written text, as when a phone transcribes a voice message or captions appear automatically under a video.
  • machine translationmachine translation, n.: Software that renders writing or speech in one language into another language, as online translation services and phone translation apps do.

Sources

  1. Jelinek, F. (1997). Statistical Methods for Speech Recognition. MIT Press.
  2. Jelinek, F., Mercer, R. L., Bahl, L. R. & Baker, J. K. (1977). Perplexity — a measure of the difficulty of speech recognition tasks. Journal of the Acoustical Society of America 62(S1): S63.