Bottleneck neural networks for Speaker Recognition



Deep neural networks are increasingly used for their capacity to correlate concrete parameters to deduce abstract characteristics. The bottleneck neural network is a specific form of those. This work presents the principle of this kind of network and its use for the reprocessing of Mel Frequency Cepstral Coefficients in a speaker recognition system. Therefore, it is a matter of studying the convergence of such network but also the change in overall system performance.