Speech recognition using time-warping neural networks

The author proposes a time-warping neural network (TWNN) for phoneme-based speech recognition. The TWNN is designed to accept phonemes with arbitrary duration, whereas conventional phoneme recognition networks have a fixed-length input window. The purpose of this network is to cope with not only variability of phoneme duration but also time warping in a phoneme. The proposed network is composed of several time-warping units which each have a time-warping function. The TWNN is characterized by time-warping functions embedded between the input layer and the first hidden layer in the network. The proposed network demonstrates higher phoneme recognition accuracy than a baseline recognizer based on conventional feedforward neural networks and linear time alignment. The recognition accuracy is even higher than that achieved with discrete hidden Markov models