A new neuron model for an Alphanet-semicontinuous HMM

The main goal is automatic speech recognition by using artificial neural networks. The authors define a generalized type of neuron that, grouped in a recurrent neural network (an Alphanet), implements a semicontinuous hidden Markov model (SCHMM). The neurons are grouped in a single layer that generates the Alphanet in such a way that some of its inputs come from the outputs. The network allows an interpretation according to SCHMM models, evaluating symbol sequences that constitute the second type of inputs. The network is trained using the backpropagation algorithm and has been applied to an isolated word recognition task. The experimental results show recognition rates reaching multi-speaker recognition rates of 97.81%.<>