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
Deep neural networks
Bibliography
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...
Directory
Manufacturers – Suppliers – Distributors (non-exhaustive list)
Companies specializing in automatic speech processing:
Vecsys http://www.vecsys.fr/presentation/index.htm
Loquendo http://www.loquendo.com/fr/
...Documentation
Speech Communication magazine (4 issues/year)
IEEE Transactions on Pattern Recognition and Machine Intelligence (6 issues/year)
International Journal of Pattern Recognition and Artificial Intelligence (4 issues/year)
Journal of the Acoustical Society of America (12 issues/year)
Traitement du Signal magazine (4 issues/year)...
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