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- W1573546415 abstract "Abstract The purpose of the project described here is to find what segments a network can discover by itself, using only prediction as a teacher. A recurrent network was trained to do a prediction task, using a speech spectrogram as both input and teacher signals. The error vector and hidden unit activation transitions were used to extract segments from the multi-speaker, continuous speech TIMIT database. The network was analysed to see what speech segments it discovered. Many of the segments the network found correspond to TIMIT phones and are very well segmented, but some TIMIT phones are not extracted. We are examining the use of these segments to drive a second network to label speech. Early results are encouraging because the system gives the same error rate whether network segments or the segments provided with the TIMIT database are used." @default.
- W1573546415 created "2016-06-24" @default.
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- W1573546415 date "1991-01-01" @default.
- W1573546415 modified "2023-09-27" @default.
- W1573546415 title "UNSUPERVISED DISCOVERY OF SPEECH SEGMENTS USING RECURRENT NETWORKS" @default.
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- W1573546415 doi "https://doi.org/10.1016/b978-1-4832-1448-1.50036-6" @default.
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