Fundamentals of speech recognition
Fundamentals of speech recognition
Automatic segmentation and labeling of speech based on Hidden Markov Models
Speech Communication
Norm-induced shell-prototypes (NISP) clustering
Neural, Parallel & Scientific Computations
Speaker-independent recognition of isolated words using rough sets
Information Sciences: an International Journal - From rough sets to soft computing
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Experiments with Rough Sets Approach to Speech Recognition
ISMIS '99 Proceedings of the 11th International Symposium on Foundations of Intelligent Systems
Intelligent Processing of Stuttered Speech
Journal of Intelligent Information Systems
Improving the intelligibility of dysarthric speech
Speech Communication
Automatic Singing Voice Recognition Employing Neural Networks and Rough Sets
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Speaker-independent phoneme alignment using transition-dependent states
Speech Communication
Speech segmentation using regression fusion of boundary predictions
Computer Speech and Language
An overview of text-independent speaker recognition: From features to supervectors
Speech Communication
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The current work describes a phoneme matching algorithm based on rough set concepts. The objective of this type of algorithms is focused on the localization of the phonemic content of a specific spoken occurrence. According to the proposed algorithm, a number of rough sets containing the multiple expected phonemic instances in a sequence are created, each defined by a set of short term frames of the voice signal. The properties of the corresponding information system are derived from a features set calculated from the speech signal upon initiation. Given the above, an iterative procedure is applied by updating the phoneme instances versus the optimization of the accuracy metric. The main advantage of this algorithm is the absence of a training phase allowing for wider speaker adaptability and independency. The current paper focuses on the feasibility of the task as this work is still in early research stage.