I made a textgrid of the sentence "I quite like cheese a lot." and created three tiers and marked the sentence, word (cheese) and the nucleus of cheese to examine the f0. Then I used a script to extract f0 at different time points between 0 to 100%.

In a specific case, I segmented the word cheese from the sentence. I followed these steps:

Extract selected sounds (preserve time) → extract selected textgrid → then saved them both.

The resultant f0 data at different timepoints from 0% to 100% is now is different, not much but like .24 each time.

My question is why with the same data, once within a sentence and once standalone, I am getting differnet f0 readings?

Is there a way to resolve this issue to get exactly same result from them?

1 Answer 1


I sort of replicated this, and noticed from the pitch listings that the start point of defined pitch differed a little bit: 15.552548 sec vs. 15.551652, therefore the F0 value also differed a little (148.186206 vs. 148.921995). This suggests that "times" are not exactly preserved (which it could be if Praat kept track of sample number as an integer and computed time as a function of sample rate). In the editor, you can take textgrids out of the picture and just manually select start and end – the reported time of the first defined pitch value will differ by a tiny amount depending on the "exact" time of the selection. Reading this and this, about the pitch tracking algorithm, may explain the underlying theory of F0 extraction and how the output value depends on a "window". I also believe that a quest for "absolute accuracy" is doomed, and you have to accept values that are "good enough".

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