The following schema describes the creation of language identification feature files that you can use to create and optimize language identification classifiers.
[langIdFeature] 0 = a <- audio(MONO, input) 1 = f1 <- frontend1(_, a) 2 = nf1 <- normalizer1(_, f1) 3 = w <- audiopreproc(A, a, nf1) 4 = f2 <- frontend2(_, a) 5 = nf2 <- normalizer2(_, f2, w) 6 = lf1 <- lidfeature(_, nf2) 7 = lf2 <- filter(LF_INCLUSIVE, lf1, w) 8 = output <- lfout(_, lf2) DefaultResults=out
0
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The audio module processes the mono audio data. |
1
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The frontend1 module converts the audio data from 0 into front-end frame data. |
2
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The normalizer1 module normalizes the frame data from 1 . |
3
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The audiopreproc1 module runs audio classification on the audio (a ) and the normalized frame data from 2 (nf1 ). |
4
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The frontend2 module converts the audio data from 0 into front-end frame data. |
5
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The normalizer2 module normalizes the frame data from 4 . |
6
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The lidfeature module converts the normalized frame data from 5 (nf2 ) into language identification feature data. |
7
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The filter module filters the output from 6 (lf1 ), using the audio classification data (w ), to include only language features that occur in segments that contain speech. |
8
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The lfout module writes the language identification feature data from 7 (lf2 ) to the output file. |
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