Phoneme recognition using time-warping neural networks.
Kiyoaki Aikawa · Journal of the Acoustical Society of Japan (E) · 1992
This paper proposes a novel neural network architecture for phoneme-based speech recognition.The new architecture is composed of five time-warping sub-networks and an output layer which integrates the sub-networks.Each time-warping sub-network has a different time-warping function embedded between the input layer and the first hidden layer.A time-warping sub-network recognizes the input speech warping the time axis using its time-warping function.The network is called the Time-Warping Neural Network (TWNN).The purpose of this network is to cope with the temporal variability of acoustic-phonetic features.The TWNN demonstrates a higher phoneme recognition accuracy than a baseline recognizer composed of time-delay neural networks with a linear time alignment mechanism.