Figure 2.
Segmentation and segment validation. (A), an example of an output of our segmentation algorithm on human speech, in this case, an English vocal breath group, “four oceans”. (B), an example the segmentation algorithm applied to budgerigar song. We scaled the algorithm to the different species by making the algorithm dependent on a minimum fundamental frequency setting. For each cluster size of human speech (C), silhouette widths were higher for segments than for syllables ( = 0.42–0.25). For the budgerigar units, we included simple syllables and random snippets of syllables that were equally long as the segments as controls (D). Silhouette width scores for segments were statistically different from complex syllables ( = 0.32–0.13, V = 105, p < 0.001) and the random snippets ( = 0.32–0.22, V = 105, p < 0.001). We created the image using Praat, R, and the R packages ggplot2 and magick18,19,25,26.