implementation of GOP with Kaldi

[Hunt Rui]

Hi Dan,

    I am trying to implement GOP with Kaldi. The procedures I am taking are listed below:

    1) Force-align and use ali-to-phones to get phones 

    2) Use 2-gram phone decoder to get the phones and use lattice-to-post and post-to-phone-post to get the posterior prob of phones

    3) Use get-post-on-ali to obtain the prob of each phone at frame level, and average the prob of each phone from all of its corresponding frames.

    Does this make sense? I also found that the phones decoded from my 2-gram phone decoder don't make sense sometimes, not all the time. Actually I used the acoustic model trained from TED and built HCLG graph from 2-gram phones of TED speech materials, and use nnet3-latgen-faster to decode it. Should I use 3-gram instead?

    

    Also I tried to implement GOP with another approach by calculating decodable.LogLikelihood(frame, tid) for each frame, and aggregated it, and the number doesn't look right. I don't know whether this approach makes sense or not.

    I have struggled over this for some time, and your time and help is well appreciated. 

Thanks,

Andy

    

[Daniel Povey]

    I am trying to implement GOP with Kaldi. The procedures I am taking are listed below:

    1) Force-align and use ali-to-phones to get phones 

    2) Use 2-gram phone decoder to get the phones and use lattice-to-post and post-to-phone-post to get the posterior prob of phones

    3) Use get-post-on-ali to obtain the prob of each phone at frame level, and average the prob of each phone from all of its corresponding frames.

    Does this make sense? I also found that the phones decoded from my 2-gram phone decoder don't make sense sometimes, not all the time. Actually I used the acoustic model trained from TED and built HCLG graph from 2-gram phones of TED speech materials, and use nnet3-latgen-faster to decode it. Should I use 3-gram instead?

Sounds reasonable.  I think you'll have to use a stronger LM; phones tend to be acoustically quite ambiguous so the

LM needs to be reasonable, to get anything accurate.

    

    Also I tried to implement GOP with another approach by calculating decodable.LogLikelihood(frame, tid) for each frame, and aggregated it, and the number doesn't look right. I don't know whether this approach makes sense or not.

    I have struggled over this for some time, and your time and help is well appreciated. 

log-likelihoods are quantities that, in general, won't even sum to one; and their values can be very different depending what type of model you have, whether you have a decision tree, the the prior, etc.  Getting something that makes sense is complicated.  If I were you I would focus on your first approach.

Dan 

Thanks,

Andy

    

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[Hunt Rui]

Hi Dan,

I got it, and thank you so much for your comments.

Andy

To view this discussion on the web visit https://groups.google.com/d/msgid/kaldi-help/CAEWAuySWQ2rTLwu9wEaE%2B7QiyNXiuRakO7S21%3D3QTjN9cUUdvw%40mail.gmail.com.

[achintyaha]

Hi Andy,

I am also working on evaluating pronunciation (I'm a beginner) so just wanted to know about the accuracy of GOP implementation with kaldi?

Thanks