The system efficiently utilizes processing resources and improves speech recognition performance by using neural networks as the classifier of the system.
It maximizes the likelihood of the data from testing environments, and allows global optimization of the neural network when used with HMM-based recognizers.
This paper proposes a robust, speaker-independent isolated word speech recognition (IWSR) system (SMQ/HMM-SVQ/HMM)/MLP which combines dual split matrix ...
Acoustic modeling in state-of-the-art speech recognition systems usually relies on hidden Markov models (HMMs) with Gaussian emission densities.
The system efficiently utilizes processing resources and improves speech recognition performance by using neural networks as the classifier of the system.
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Mar 27, 2000 · This paper proposes a robust, speaker-independent isolated word speech recognition (IWSR) system (SMQ/HMM_SVQ/HMM)/MLP which combines Dual ...
This report presents an overview of a program of speech recognition research which was initiated in 1985 with the major goal of developing techniques for ...
Jan 3, 2023 · Neural networks offer several advantages over hidden Markov models (HMMs) in the domains of speech recognition and text-to-speech (TTS) ...
This report presents an overview of a program of speech recognition research which was initiated in 1985 with the major goal of developing techniques for robust ...
Jan 13, 2018 · HMMs are more accurate than neural networks when learning from limited data. Disadvantages of using a Hidden Markov Model (HMM):. HMMs are not ...