6. Robust recognition methods

6.1 Problem position

The performance of currently available recognition systems is good under well-controlled conditions of use. This performance is highly dependent on the complexity and difficulty of the task in hand. Thus, on average, error rates measured in the laboratory, in speaker-independent mode, can range from 0.3% (for sequences of numbers) to 5% (for a vocabulary of 20,000 words in continuous speech), then to 8% (for spelled-out letters), and up to 55% for spontaneous telephone conversations!

Furthermore, the error rate increases dramatically when the conditions under which a system is trained and used are different (particularly with regard to noise type and level).

As an example, figure...

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Robust recognition methods

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Bibliography

(1) - RABINER (L.), HUANG (B.H.) - Fundamentals of speech recognition. – - Prentice-Hall, Englewood Cliffs (1993).
(2) - JUNQUA (J.-C.), HATON (J.-P.) - Robustness in automatic speech recognition. – - Kluwer Academic, Dordrecht...

Software tools

HTK (HMM ToolKit): open-source software for the development of complete speech recognition applications based on MMC http://www.htk.eng.cam.ac.uk/

VISPER (Visual speech processing system): free software for visualizing dynamic programming and MMC recognition stages, developed by the Technical University of Liberec, Czech...