Phoneme recognition using time-warping neural networks

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概要

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• This paper proposed 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.

• 社団法人日本音響学会の論文

著者

• Aikawa Kiyoaki

  Ntt Human Interface Laboratories

関連論文

• Phoneme recognition using time-warping neural networks

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