Robust Speaker Detection Using Neural Networks
John Shell · ASME Press eBooks · 2006
The work proposed in this paper utilizes Neural Networks to distinguish speech patterns. A feature extractor is used as a standard Linear Processing Coefficients (LPC) Cepstrum coder, converting the incoming speech signal captured by a Matlab interface into LPC Cepstrum feature space. A Neural Network makes each variable length LPC trajectory of an isolated word into a fixed length LPC trajectory, providing the fixed length feature vector that is fed into a recognizer. The recognizer uses a Feed Forward (FF) and Back Propagation (BP) Network approach to test the signal output for the recognition of the feature vectors of isolated words. The feature vector was normalized and de-correlated. Momentum is used to find the global minima of the error surface avoiding oscillations in local minima. The goal of the work is to consistently identify a randomly chosen speech pattern from the samples of four different speakers 100% of the time