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- W1987841215 abstract "Convolutional neural networks have proved very successful in image recognition, thanks to their tolerance to small translations. They have recently been applied to speech recognition as well, using a spectral representation as input. However, in this case the translations along the two axes - time and frequency - should be handled quite differently. So far, most authors have focused on convolution along the frequency axis, which offers invariance to speaker and speaking style variations. Other researchers have developed a different network architecture that applies time-domain convolution in order to process a longer time-span of input in a hierarchical manner. These two approaches have different background motivations, and both offer significant gains over a standard fully connected network. Here we show that the two network architectures can be readily combined, like their advantages. With the combined model we report an error rate of 16.7% on the TIMIT phone recognition task, a new record on this dataset." @default.
- W1987841215 created "2016-06-24" @default.
- W1987841215 creator A5020608163 @default.
- W1987841215 date "2014-05-01" @default.
- W1987841215 modified "2023-09-25" @default.
- W1987841215 title "Combining time- and frequency-domain convolution in convolutional neural network-based phone recognition" @default.
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- W1987841215 doi "https://doi.org/10.1109/icassp.2014.6853584" @default.
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