Similarity Match (SM) technique for the development of client barcode

Abstract

A hybrid neural network is proposed for speaker verification (SV). The basic idea in this system is the usage of vector quantization preprocessing as the feature extractor. The experiments were carried out using a neural network model (NNM) with frame labeling performed from a client codebook known as NNM-C. The work also examines how the neural network model with enhance features from the client barcode compares to NNM client codebook with linear time normalization (LTN). Improved performance for NNM (client barcode) with more inputs and proper alignment of the speech signals supports the hypothesis that a more detailed representation of the speech patterns proved helpful for the system. The flexibility of this system allows an equal error rate (EER) of 0.62% (speaker specific EER) on a single isolated digit and 1.9% (SI EER) on a sequence of 12 isolated digits