On the Use of Artificial Neural Networks in Automatic Speech Recognition, M.Sc. Thesis Sharif University of Technology ; Ghorshi, Mohammad Ali (Supervisor) ; Khayyat, Amir Ali Akbar (Supervisor)
Abstract
In this thesis, the Artificial Neural Networks (ANN) will be used in Automatic Speech Recognition (ASR) instead of Hidden Markov Models (HMM). Hidden Markov Model is one of the most dominant Bayesian network technologies and is the most successful model in current ASR systems. However, excessive training time is a major issue in speech recognition based on Hidden Markov Model (HMM). This thesis presents an Artificial Neural Network language model for human speech by mapping the spectral features of speech namely the formants, cepstrum (Mel-Frequency Cepstral Coefficients (MFCCs)) and Power Spectral Density (PSD) as features of samples of specific words into a discrete vector space. The...
Cataloging briefOn the Use of Artificial Neural Networks in Automatic Speech Recognition, M.Sc. Thesis Sharif University of Technology ; Ghorshi, Mohammad Ali (Supervisor) ; Khayyat, Amir Ali Akbar (Supervisor)
Abstract
In this thesis, the Artificial Neural Networks (ANN) will be used in Automatic Speech Recognition (ASR) instead of Hidden Markov Models (HMM). Hidden Markov Model is one of the most dominant Bayesian network technologies and is the most successful model in current ASR systems. However, excessive training time is a major issue in speech recognition based on Hidden Markov Model (HMM). This thesis presents an Artificial Neural Network language model for human speech by mapping the spectral features of speech namely the formants, cepstrum (Mel-Frequency Cepstral Coefficients (MFCCs)) and Power Spectral Density (PSD) as features of samples of specific words into a discrete vector space. The...
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