The paper studies the use of articulatory-acoustic features in a system perform-ing classification on syllables, using the TIMIT speech corpus. The articulatory-acoustic feature data was extracted through the use of artificial neural networks, and then used to classify syllables instead of the more traditional phone. A series of experiments on this type of classification are reported here, including template matching. ii Declaration I hereby declare that this thesis is of my own composition, and that it contains no material previously submitted for the award of any other degree. The work reported in this thesis has been executed by myself, except where due acknowledge-ment is made in the text
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