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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/14958

Title: Inhibition/Enhancement of Articulatory features - Which one is Dominant for Speech Recognition
Authors: Ghulam Muhammad
Mohammad Mahedi Hasan
Keywords: inhibition/enhancement, multilayer neural network, hidden markov model, articulatory features, local features
Issue Date: 2010
Publisher: IEEE Computer Society
Abstract: This paper presents a speech recognition technique based on inhibition/enhancement (In/En) of articulatory features (AFs) by determining the dominant factor between inhibition and enhancement. The proposed method comprises three stages - a) Multilayer Neural Networks (MLNs), b) In/En Network and c)Gram-Schmidt (GS) Orthogonalization. At first stage, the MLNs detects AFs and then In/En network is used to achieve categorical articulatory movement by enhancing peak patterns and inhibiting dip patterns. Finally GS algorithm decor relates the modified features to obtain orthogonalized feature vector before connecting with a hidden Markov model (HMM) based classifier. From the experiments based on phoneme recognition, it is shown that the enhancement of AFs affects more on Phoneme Recognition than Inhibition.
URI: http://hdl.handle.net/123456789/14958
Appears in Collections:College of Computer and Information Sciences

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