The Concise Encyclopedia of Applied Linguistics. Carol A. Chapelle

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Название The Concise Encyclopedia of Applied Linguistics
Автор произведения Carol A. Chapelle
Жанр Языкознание
Серия
Издательство Языкознание
Год выпуска 0
isbn 9781119147374



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      14 Snow, M. A., & Brinton, D. (Eds.). (2017). The content‐based classroom: New perspectives on integrating language and content. Ann Arbor: University of Michigan Press.

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      1 Maley, A., & Tomlinson, B. (Eds.). (2017). Authenticity in materials development for language learning. Newcastle upon Tyne, England: Cambridge Scholars Publishing.

      2 Mishan, F. (2005). Designing authenticity into language learning materials. Bristol, England: Intellect.

      3 Rilling, S., & Dantas‐Whitney, M. (Eds.). (2009). Authenticity in the language classroom and beyond: Adult learners. Alexandra, VA: TESOL.

      4 Wagner, E., & Ockey, G. J. (2018). An overview of the use of authentic, real‐world spoken texts on L2 listening tests. In G. J. Ockey & E. Wagner (Eds.), Assessing L2 listening: Moving towards authenticity (pp. 13–28). Amsterdam, Netherlands: John Benjamins.

      1 Based in part on S. McKay (2012). Authenticity in the language teaching curriculum. In C. A. Chapelle (Ed.), The Encyclopedia of Applied Linguistics. John Wiley & Sons Inc., with permission.

      JOHN LEVIS AND RUSLAN SUVOROV

      Automatic speech recognition (ASR) is an independent, machine‐based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker through a microphone; analyzes it using some pattern, model, or algorithm; and produces an output, usually in the form of a text (Lai, Karat, & Yankelovich, 2008).

      It is important to distinguish speech recognition from speech understanding, the latter being the process of determining the meaning of an utterance rather than its transcription. Speech recognition is also different from voice (or speaker) recognition: Whereas speech recognition refers to the ability of a machine to recognize the words and phrases that are spoken (i.e., what is being said), speaker (or voice) recognition involves the ability of a machine to recognize the person who is speaking.

      Pioneering work on ASR dates to the early 1950s. The first ASR system, developed at Bell Telephone Laboratories by Davis, Biddulph, and Balashek (1952), could recognize isolated digits from 0 to 9 for a single speaker. In 1956, Olson and Belar created a phonetic typewriter that could recognize 10 discrete syllables. It was also speaker dependent and required extensive training.

      These early ASR systems used template‐based recognition based on pattern matching that compared the speaker's input with prestored acoustic templates or patterns. Pattern matching operates well at the word level for recognition of phonetically distinct items in small vocabularies but is less effective for larger vocabulary recognition. Another limitation of pattern matching is its inability to match and align input speech signals with prestored acoustic models of different lengths. Therefore, the performance of these ASR systems was lackluster because they used acoustic approaches that only recognized basic units of speech clearly enunciated by a single speaker (Rabiner & Juang, 1993).

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      The main strength of an HMM is that it can describe the probability of states and represent their order and variability through matching techniques such as the Baum‐Welch or Viterbi algorithms. In other words, HMMs can adequately analyze both the temporal and spectral variations of speech signals, and can recognize and efficiently decode continuous speech input. However, HMMs require extensive training and huge computational power for model‐parameter storage and likelihood evaluation (Burileanu, 2008).