Usually machine learning algorithms are not trained with raw data (aka end-to-end) but with features that model the entities of interest.
With respect to speech samples these features might be for example average pitch value over the whole utterance or length of utterance.
Now if the pitch value is given in Hz and the length in seconds, the pitch value will be in the range of [80, 300] and the length, say, in the range of [1.5, 6].
Machine learning approaches now would give higher consideration on the avr. pitch because the values are higher and differ by a larger amount, which is in the most cases not a good idea because it's a totally different feature.
A solution to this problem is to scale all values so that the features have a mean of 0 and standard deviation of 1.
This can be easily done with the preprocessing API from sklearn:
from sklearn import preprocessing scaler = StandardScaler() scaled_features = preprocessing.scaler.fit_transform(features)
Be aware that the use of the standard scaler only makes sense if the data follows a normal distribution.