Monthly Archives: June 2022

nkululeko, tutorial

How to combine feature sets with Nkululeko

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If you want to use combine several acoustic parameter (feature) sets with nkululeko, you might state

[FEATS]
type = ['mld', 'praat']
features = ['JitterPCA', 'meanF0Hz', 'hld_sylRate']

This would combine the

  • hld_sylRate feature from MLD
  • JitterPCA feature from Feinberg's Praat features and
  • meansF0Hz feature from Feinberg's Praat features

Of course you could omit the features entry and simply use all of them.

It's interesting to see how many emotions from Berlin Emodb can still be recognized with only these three parameters:

Allgemein, nkululeko, tutorial

How to use selected features from Praat with Nkululeko

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If you want to use acoustic parameters extracted by the wonderful Praat software with nkululeko, you state

[FEATS]
type=['praat']

in the feature section of your config file.
If you like to use only some features of all the ones that are extracted by David R. Feinberg's Praat scripts, you can look at the output and select some of them in the FEAT section, e.g.

type = ['praat']
praat.features = ['speechrate(nsyll / dur)']

You can do the same with opensmile features:

type = ['os']
os.features = ['F0semitoneFrom27.5Hz_sma3nz_amean']

or even combine them

type = ['praat', 'os']
praat.features = ['speechrate(nsyll / dur)']
os.features = ['F0semitoneFrom27.5Hz_sma3nz_amean']

this is actually the same as

type = ['praat', 'os']
features = ['speechrate(nsyll / dur)', 'F0semitoneFrom27.5Hz_sma3nz_amean']

if you would want to combine all of opensmile eGeMAPS features with selected Praat features, you would do:

type = ['praat', 'os']
praat.features = ['speechrate(nsyll / dur)']

It is interesting to see, how many emotions of Berlin EmoDB still get recognized with only mean F0 and Jitter as features:

image

What kind of features are there, you might ask yoursel?
Here's a list:
'duration', 'meanF0Hz', 'stdevF0Hz', 'HNR', 'localJitter',
'localabsoluteJitter', 'rapJitter', 'ppq5Jitter', 'ddpJitter',
'localShimmer', 'localdbShimmer', 'apq3Shimmer', 'apq5Shimmer',
'apq11Shimmer', 'ddaShimmer', 'f1_mean', 'f2_mean', 'f3_mean',
'f4_mean', 'f1_median', 'f2_median', 'f3_median', 'f4_median',
'JitterPCA', 'ShimmerPCA', 'pF', 'fdisp', 'avgFormant', 'mff',
'fitch_vtl', 'delta_f', 'vtl_delta_f''

nkululeko, tutorial

How to test a trained model on a new test set with Nkululeko

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Sometimes you might want to test your already trained model(s) on a new dataset, e.g. because the training took a lot of resources.
If you stored your models during the training this is possible.

[DATA]
databases = ['emodb']
....
[MODEL]
save = True

In a new config file for your experiment that uses a dufferent test set, you set

[DATA]
databases = ['emodb', 'polish']
trains = ['emodb']
tests = ['polish']
strategy = cross_data....
[MODEL]
only_test = True

In the example above, emodb has been used as the training database, and polish in a second experiment later as a test database.