Speaker Recognition Using Pulse Coupled Neural Networks

Pulse coupled neural network (PCNN) is a paradigm that has not yet been explored enough in speaker recognition. This paper presents the results of experiments conducted to develop a new recognition architecture that applies PCNN to text independent speaker recognition. The proposed architecture comprises a two layer PCNN for feature extraction and a multilayer perceptron (MLP) for final classification. The first layer of PCNN performs a pulse coding task and its outputs are used as inputs to a second self-organizing layer that learns the pulse´s statistics. Initially, the well known Mel-cepstral frequency coefficients were used as a benchmark to verify the new architecture´s capability of learning the speaker information. After these preliminary tests, the Mel scaled filter bank energy coefficients were used, resulting in a biologically plausible architecture. The first results demonstrated the PCNN´s ability to deal with temporal information contained in speech signals. The proposed architecture is promising, presenting 82% recognition when compared with 96% of the classical MLP classifier.