Md Sah Bin Hj Salam

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This paper explains works in speech recognition using neural network. The main objective of the experiment is to choose suitable number of nodes in hidden layer and learning parameters for malay iIsolated digit speech problem through trial and error method. The network used in the experiment is feed forward multilayer perceptron trained with back(More)
Speech signal is temporally and acoustically varies. Recognition of speech by static input Neural Network requires temporal normalization of the speech to be equal to the number of input nodes of the NN while maintaining the properties of the speech. This paper compares three methods for speech temporal normalization namely the linear, extended linear and(More)
Problem statement: Speech segmentation is an important part for speech recognition, synthesizing and coding. Statistical based approach detects segmentation points via computing spectral distortion of the signal without prior knowledge of the acoustic information proved to be able to give good match, less omission but lot of insertion. These insertion(More)
This paper describes an alternative approach in solving connected digit problem in speech recognition. Instead of depending very much on the validity of the speech signal via segmentation of isolated digit; this work applies a genetic like approach to anticipate missing or unrecognized acoustic information prior to recognize the whole speech string being(More)
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