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OF THE DISSERTATION Robust Speech Recognition Using Neural Networks and Hidden Markov Models Adaptations Using Non-linear Transformations by DongSuk Yuk Dissertation Directors: Dr. Casimir Kulikowski and Dr. James Flanagan When the training and testing conditions are not similar, statistical speech recognition algorithms suffer from severe degradation in(More)
In this paper, we present several new robust isolated word speech recognition systems which employ FMQ/MQ as the spectral labelling process, followed by a Hidden Markov Model (HMM), or a HMM and Neural Network (HMM/MLP) classification technique. The ISWR systems provide selective input data to a neural network in response to speech signal to acoustic noise(More)
This paper proposes a robust speaker-independent, connected digit recognition system for mobile applications. The system requires a small amount of ROM and low computational cost with high recognition accuracy. In addition, the system can be efficiently implemented on most currently available 32-bit fixed-point DSP chips. To reach these goals, we combined(More)
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