Sometimes, with categorically labeled data, the number of samples per class is very unevenly distributed, misleading the model to think that the overwhelming majority class is more important than the others.
In this case, two techniques might help: class weighting assigns a higher weight to samples from minority classes, and oversampling "invents" new samples for the minority classes.
With nkululeko since version 0.70.0, you can oversample the trainig set with different algorithms implemented by the imb_learn package.
You simply state the method in the FEATS section like so:

[FEATS]
...
balancing = adasyn # either ros, smote or adasyn

These methods are available:

• ros: simply repeat random samples from the minority classes
• smote: "invent" new minority samples by little changes from the existing ones
• adasyn: similar to smote, but resulting in uneven class distributions
• borderlinesmote: SMOTE variant focusing on borderline instances
• svmsmote: SMOTE variant using SVM for generating synthetic samples

Under-sampling methods (reduce majority classes):

• clustercentroids: replace majority class clusters with their centroids using K-means clustering
• randomundersampler: randomly remove samples from majority classes
• editednearestneighbours: remove noisy samples using edited nearest neighbors
• tomeklinks: remove Tomek links to clean class boundaries

Combination methods (over-sampling + under-sampling):

• smoteenn: combination of oversampling with SMOTE and undersampling with edited nearest neighbour (ENN)
• smotetomek: combination of SMOTE oversampling and Tomek links undersampling

With nkululeko since version 0.68.1, you can re-name data fields (columns in your data table) by setting the following in your ini-file:

[DATA]
databases = ['mydata']
mydata.colnames = {'Participant ID':'speaker', 'sex':'gender', 'Age': 'age'}

which means, that, before further processing, the Participant ID field in your database mydata will be treated as speaker label and so on.

With nkululeko since version 0.68.0, the selection of test/dev vs. train samples can be done automatically in a stratified manner, i.e. trying to find splits that are age or gender balanced.
An example for such a configuration is this:

[DATA]
# the name of the database
databases = ['emodb']
# the location of the data
emodb = ./data/emodb/emodb
# set the split strategy to "balanced"
emodb.split_strategy = balanced
# set a percentage value for your test split
emodb.test_size = 20
# stratify variables with weights for importance
balance = {'emotion':2, 'age':1, 'gender':1}
# all stratification variables need to be categorical, 
# so we need to state the number of bins for "age" 
age_bins = 2
# a value for how much importance to give for the ideal group sizes
size_diff_weight = 1
# the target value of the experiment
target = emotion

Nkululeko will always keep the speaker variable disjunct, i.e. resulting splits will contain different speakers.
With the example above, the algorithm will try to balance emotion, gender and (binned) age distributions across the splits.