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- W1034769720 abstract "The aim of this paper is to present a hybrid algorithm that combines the advantages of artificial neural networks and hidden Markov models in speech recognition for control purpos- es. The scope of the paper includes review of currently used solutions, description and analysis of implementation of selected artificial neural network (NN) structures and hidden Markov mod- els (HMM). The main part of the paper consists of a description of development and implementation of a hybrid algorithm of speech recognition using NN and HMM and presentation of verification of correctness results. owadays, a fast and reliable communication with electrical equipment plays important role. Despite the fact that the easiest and most intuitive form of com- munication and command is speech, the most common in communication with the devices are methods based on mechanical effects on the control such as keyboard, or joystick. The current knowledge allows the realization of voice control systems, which was not possible a few years ago. Therefore, there is a need to develop more efficient methods of human speech recognition, to ensure the relia- bility of communication between man and machine. This paper describes an approach using mel-cepstral coefficients (MFCC) as the basis for the analysis of the speech signal. Based on the resulting signal characterizing factors are identified appropriate elements (sounds / words) using a hybrid algorithm, combining the benefits of artificial neural network (NN) and hidden Markov models (HMM)." @default.
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- W1034769720 date "2013-01-01" @default.
- W1034769720 modified "2023-09-23" @default.
- W1034769720 title "Hybrid of Neural Networks and Hidden Markov Models as a modern approach to speech recognition systems" @default.
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