Discrete-time signal processing
Discrete-time signal processing
Hidden Markov models for speech recognition
Technometrics
Fundamentals of speech recognition
Fundamentals of speech recognition
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Data Signal Processing: DSP and Applications
Data Signal Processing: DSP and Applications
Linear System Theory and Design
Linear System Theory and Design
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
On the use of support vector machines for phonetic classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Recognition of greek phonemes using support vector machines
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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One of the main difficulties in the speech recognition process is the treatment of the imprecisions around it. They have origin in the differences between the articulatory system of each person and the physical properties of the sound propagation. Moreover, the circuits involved in the sound storing and analysis works with a degree of uncertainty and also adds some imprecision in the process. This paper discuss the applications of mathematical methods used to treat uncertainty in the computational speech recognition and the possibility of using interval analysis for this propose.