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Phoneme recognition using time-warping neural networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kiyoaki Aikawa |
| Copyright Year | 2010 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.jstage.jst.go.jp/article/ast1980/13/6/13_6_395/_pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Acoustic cryptanalysis Apache Axis Artificial neural network Baseline (configuration management) Biological Neural Networks Embedded system Embedding Finite-state machine Heart rate variability Loudspeaker time alignment Network architecture Neural Network Simulation Phoneme Speech recognition Subnetwork Time complexity |
| Content Type | Text |
| Resource Type | Article |
