MFCC vs FBANK for chain models ?

[Titouan Parcollet]

Hi there!

I was wondering why is it still preferred to use mfcc_hires over standard fbanks for training nnet3 chain models? With neural networks, we don't need the features to be decorrelated. Does anyone has compared both features with a medium/big dataset?

Thank you!

[Daniel Povey]

The reason we use MFCC is because they are more easily compressible, being decorrelated; we dump them to disk

with compression to 1 byte per coefficient.  But we dump all the coefficients, so it's equivalent to filterbanks times

a full-rank matrix, no information is lost.  For convolutional architectures, we convert  them back into filterbanks inside the 

network; see idct-layer.

Dan

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[Titouan Parcollet]

Thank you! 

Le dimanche 14 juillet 2019 20:21:08 UTC+2, Dan Povey a écrit :

The reason we use MFCC is because they are more easily compressible, being decorrelated; we dump them to disk

with compression to 1 byte per coefficient.  But we dump all the coefficients, so it's equivalent to filterbanks times

a full-rank matrix, no information is lost.  For convolutional architectures, we convert  them back into filterbanks inside the 

network; see idct-layer.

Dan

On Sun, Jul 14, 2019 at 11:19 AM Titouan Parcollet <parcolle...@gmail.com> wrote:

Hi there!

I was wondering why is it still preferred to use mfcc_hires over standard fbanks for training nnet3 chain models? With neural networks, we don't need the features to be decorrelated. Does anyone has compared both features with a medium/big dataset?

Thank you!

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[范璐]

Hi，Dan.

Is idct-layer only compatible for convolutional layers or idct-layer can be used before any layers? e.g. tdnn, tdnnf and so on

在 2019年7月15日星期一 UTC+8上午2:21:08，Dan Povey写道：

The reason we use MFCC is because they are more easily compressible, being decorrelated; we dump them to disk

with compression to 1 byte per coefficient.  But we dump all the coefficients, so it's equivalent to filterbanks times

a full-rank matrix, no information is lost.  For convolutional architectures, we convert  them back into filterbanks inside the 

network; see idct-layer.

Dan

On Sun, Jul 14, 2019 at 11:19 AM Titouan Parcollet <parcolle...@gmail.com> wrote:

Hi there!

I was wondering why is it still preferred to use mfcc_hires over standard fbanks for training nnet3 chain models? With neural networks, we don't need the features to be decorrelated. Does anyone has compared both features with a medium/big dataset?

Thank you!

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[Daniel Povey]

There's no point using idct-layer before layers like tdnn-f.  The only difference it might make is by reversing the 'liftering'

whereby, in normal MFCCs, the higher-frequency components are scaled up.  But I suspect that would degrade, not

improve, WERs, if anything.

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[Lu Fan]

the conv layers is just compute 3*3 on input seq*freq.

 tdnn is compute on all freq of concated frames like (-1,0,1).So tdnn computation is like dct or idct. right?

Another question. Is specaugment compatible for mfccs?

在 2019年7月16日星期二 UTC+8上午2:01:49，Dan Povey写道：

[Daniel Povey]

the conv layers is just compute 3*3 on input seq*freq.

 tdnn is compute on all freq of concated frames like (-1,0,1).So tdnn computation is like dct or idct. right?

No.

 

Another question. Is specaugment compatible for mfccs?

you would do it on filterbanks using idct-layer, see the example in mini_librispeech.

 

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[Lu Fan]

Yes, I want to use tdnn_lstm chain model with SpecAugment. not the conv-tdnnf model.

MFCC-> idct-layer -> SpecAugment -> tdnn_lstm, Is this flow correct?

But There's no point using idct-layer before layers like tdnn-f. So I was confused.

在 2019年7月16日星期二 UTC+8上午10:15:15，Dan Povey写道：

[Daniel Povey]

Yes that's fine.

There is no point before TDNN-F, no, because it doesn't care, but there is a point when it's before SpecAugment.
Dan

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[Lu Fan]

I see. Thanks very much.

在 2019年7月17日星期三 UTC+8上午2:03:32，Dan Povey写道：