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Speech and Handwriting Recognition

  • Chapter
Machine Learning for Audio, Image and Video Analysis

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

This chapter presents speech and handwriting recognition, i.e. two major applications involving the markovian models described in Chapter 10. The goal is not only to present some of the most widely investigated applications of the literature, but also to show how the same machine learning techniques can be applied to recognize data apparently different like handwritten word images and speech recordings. In fact, the only differences between handwriting and speech recognition systems concern the so-called front-end, i.e. the low-level processing steps dealing directly with the raw data (see Section 12.2 for more details). Once the raw data have been converted into sequences of vectors, the same recognition approach, based on hidden Markov models and N-grams, is applied to both problems and no more domain specific knowledge is needed. The possibility of dealing with different data using the same approach is one of the main advantages of machine learning, in fact it makes it possible to work on a wide spectrum of problems even in absence of deep problem specific knowledge.

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(2008). Speech and Handwriting Recognition. In: Machine Learning for Audio, Image and Video Analysis. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84800-007-0_12

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