Adaptive Activation Function for Isolated Digit Recognition Based on Speaker Dependent System
Authors: Ummu Salmah, M.H, Siti Mariyam, S., Saira Banu, O.K., Nor Azah,
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An automatic speech recognition (ASR) system has been the goal in speech research for more than 6 decades. This study focuses on developing the robustness of the MLP neural network for the Malay isolated digit recognition system by proposing a simple novel approach. An adaptive sigmoid function is implemented to achieve this objective. A typical or fixed sigmoid function method is used in the learning phase. In the recognition phase, an adaptive sigmoid function is employed. In this sense, the slope of the activation function is adjusted to gain highest recognition rate. The outcome of the simulation reveals that adaptive sigmoidal function offers a number of advantages over traditional fixed sigmoid function, resulting in better generalization performance. The proposed approach implicates ASR is applicable for the task on Malay language continuous speech and the speaker independent task to fulfill the ultimate goal in speech technology, towards natural ASR.