Binary and multi-class classification systems and methods using one spike connectionist temporal classification

Abstract

A classification training system for binary and multi-class classification comprises a neural network operable to perform classification of input data, a training dataset including pre-segmented, labeled training samples, and a classification training module operable to train the neural network using the training dataset. The classification training module includes a forward pass processing module, and a backward pass processing module. The backward pass processing module is operable to determine whether a current frame is in a region of target (ROT), determine ROT information such as beginning and length of the ROT and update weights and biases using a cross-entropy cost function and One Spike Connectionist Temporal Classification (OSCTC) cost function. The backward pass module further computes a soft target value using ROT information and computes a signal output error using the soft target value and network output value.