12 May 2016 Optimization of multilayer neural network parameters for speaker recognition
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This article discusses the impact of multilayer neural network parameters for speaker identification. The main task of speaker identification is to find a specific person in the known set of speakers. It means that the voice of an unknown speaker (wanted person) belongs to a group of reference speakers from the voice database. One of the requests was to develop the text-independent system, which means to classify wanted person regardless of content and language. Multilayer neural network has been used for speaker identification in this research. Artificial neural network (ANN) needs to set parameters like activation function of neurons, steepness of activation functions, learning rate, the maximum number of iterations and a number of neurons in the hidden and output layers. ANN accuracy and validation time are directly influenced by the parameter settings. Different roles require different settings. Identification accuracy and ANN validation time were evaluated with the same input data but different parameter settings. The goal was to find parameters for the neural network with the highest precision and shortest validation time. Input data of neural networks are a Mel-frequency cepstral coefficients (MFCC). These parameters describe the properties of the vocal tract. Audio samples were recorded for all speakers in a laboratory environment. Training, testing and validation data set were split into 70, 15 and 15 %. The result of the research described in this article is different parameter setting for the multilayer neural network for four speakers.
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Jaromir Tovarek, Jaromir Tovarek, Pavol Partila, Pavol Partila, Jan Rozhon, Jan Rozhon, Miroslav Voznak, Miroslav Voznak, Jan Skapa, Jan Skapa, Dominik Uhrin, Dominik Uhrin, Zdenka Chmelikova, Zdenka Chmelikova, "Optimization of multilayer neural network parameters for speaker recognition", Proc. SPIE 9850, Machine Intelligence and Bio-inspired Computation: Theory and Applications X, 98500C (12 May 2016); doi: 10.1117/12.2223545; https://doi.org/10.1117/12.2223545

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