Thesis (MScIng)--University of Stellenbosch, 2008. / Bibliography. Also available via the Internet.

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 47-49)

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 36-37)

Thesis (Master Diploma (Technology))--Cape Technikon, Cape Town,1993 / A study into the practical implementation of Speech Recognition for the purposes of Data Capturing within Telkom SA. is described. As datacapturing is increasing in demand a more efficient method of capturing is sought. The technology relating to Speech recognition is herein examined and practical gnidelines for selecting a Speech recognition system are described. These guidelines are used to show how commercially available systems can be evaluated. Specific tests on a selected speech recognition system are described, relating to the accuracy and adaptability of the system. The results obtained illustrate why at present speech recognition systems are not advisable for the purpose of Data capturing. The results also demonstrate how the selection of keywords words can affect system performance. Areas of further research are highlighted relating to recognition performance and vocabulary selection.

Submitted in fulfillment of the academic requirements for the degree of Master of Technology in Electrical Engineering in the Department of Electronic Engineering, Faculty of Engineering, ML Sultan Technikon of Durban in South Africa, March 2002. / The use of speaker recognition technology in interactive voice response and electronic commerce systems has been limited. This is due to the lack of research attention and published results when compared to all the other areas of speech recognition technologies / M

Speech intelligibility represents how comprehensible a speech is. It is more important than speech quality in some applications. Single channel speech intelligibility enhancement is much more difficult than multi-channel intelligibility enhancement. It has recently been reported that training-based single channel speech intelligibility enhancement algorithms perform better than Signal to Noise Ratio (SNR) based algorithm. In this thesis, a training-based Deep Neural Network (DNN) is used to improve single channel speech intelligibility. To increase the performance of the DNN, the Multi-Resolution Cochlea Gram (MRCG) feature set is used as the input of the DNN. MATLAB objective test results show that the MRCG-DNN approach is more robust than a Gaussian Mixture Model (GMM) approach. The MRCG-DNN also works better than other DNN training algorithms. Various conditions such as different speakers, different noise conditions and reverberation were tested in the thesis.

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