Practical approaches to speech coding
Practical approaches to speech coding
Using asymmetric windows in automatic speech recognition
Speech Communication
Modeling and prediction with a class of time delay dynamic neural networks
Applied Soft Computing
Invited paper: Automatic speech recognition: History, methods and challenges
Pattern Recognition
A novel objective function for improved phoneme recognition using time-delay neural networks
IEEE Transactions on Neural Networks
Continuous-time temporal back-propagation with adaptable time delays
IEEE Transactions on Neural Networks
How delays affect neural dynamics and learning
IEEE Transactions on Neural Networks
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Neural networks with fixed input length are not able to train and test data with variable lengths in one network size. This issue is very crucial when the neural networks need to deal with signals of variable lengths, such as speech. Though various methods have been proposed in segmentation and feature extraction to deal with variable lengths of the data, the size of the input data to the neural networks still has to be fixed. A novel Self-Adjustable Neural Network (SANN) is presented in this paper, to enable the network to adjust itself according to different data input sizes. The proposed method is applied to the speech recognition of Malay vowels and TIMIT isolated words. SANN is benchmarked against the standard and state-of-the-art recogniser, Hidden Markov Model (HMM). The results showed that SANN was better than HMM in recognizing the Malay vowels. However, HMM outperformed SANN in recognising the TIMIT isolated words.