Feature windowing-based for Thai text-dependent speaker identification using MLP with backpropagation algorithm

This paper presents the development of Thai text-dependent speaker identification system by applying two feature-feeding approaches. A well-known multilayer perceptron (MLP) network with backpropagation learning algorithm is chosen due to its fast processing time and good performance for pattern recognition problems. But MLP has a limitation in that a network must have a fixed amount of input nodes. Therefore, the linear interpolation time normalization is chosen to adjust the input speech signal into a fixed size of input vector. Furthermore, the windowing technique is developed to avoid the distortion caused by a time normalization process. A fixed size window is sliced through the preprocessed features with fixed amount of overlapping frames. The high identification rate observed in experiments confirms that the developed windowing is suitable for the proposed Thai text-dependent speaker identification system.