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Speech and Handwriting Recognition

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Machine Learning for Audio, Image and Video Analysis

Abstract

What the reader should know to understand this chapter \(\bullet \) Hidden Markov models (Chap. 10). \(\bullet \) Language models (Chap. 10). \(\bullet \) Bayes decision theory (Chap. 3).

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Notes

  1. 1.

    At the time this book is being written, the package can be downloaded at http://htk.eng.cam.ac.uk.

  2. 2.

    In this case, the decoding takes into account the fact that each sample corresponds to a single word and does not try to align the data with more than one word. This avoids deletion and insertion errors that are explained in the following.

  3. 3.

    Call routing is the problem of automatically finding an operator capable of addressing the needs expressed by a person contacting a call center. In this case, a perfect transcription is not necessary; the only important thing is to recognize the few keywords identifying the user needs and the right operator.

  4. 4.

    The data is publicly available and it can be downloaded at the following ftp address: ftp.eng.cam.ac.uk/pub/data.

  5. 5.

    At the time this book is being written, the proceedings are available online at the site http://nist.trec.gov.

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Camastra, F., Vinciarelli, A. (2015). Speech and Handwriting Recognition. In: Machine Learning for Audio, Image and Video Analysis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-6735-8_12

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