In this third post about linguistic tools, I’ll be discussing software that I use for acoustic analysis. Praat is one of the premier acoustic analysis tools available for computers. While there are probably commercial software products out there that are more powerful and with more bells and whistles, Praat offers some of the best ways to visualize and manipulate sound while being free and cross-platform. While it’s not completely intuitive, it is quite easy to explore the sound space of a recording, especially recorded speech, and I ran a workshop on the basics of how to use it, with online materials that you can practice with if you want to learn more. There are also other great tutorials online that you should search for.
One of the best features of Praat is the ability to segment sounds using TextGrids, which are basically text files that identify sections of a sound file using timestamps. The benefit of this is that once you have properly annotated a sound file you can use scripts to automate analyses, which saves a lot of time that would otherwise be spent taking individual measurements. When I first started my PhD I spent a good amount of time learning to write Praat scripts, which turned out to be a continuation of the programming I learned when I was younger (Basic, QBasic) and a worthy introduction to programming languages like Python.
Since this has turned out to be a post that discusses Praat scripting, I’m going to introduce/attach some of the scripts I wrote/use for acoustic analysis, and link to some of the many other places you can find scripts for your particular use case. In my case these scripts are mainly in service of documentation and description of endangered and unwritten languages, but maybe others will find them useful as well.
Automatically measuring sounds:
This script (“dur_f0_f1_f2_f3_intensity.praat”) is one that I modified (originally from this script but more recently I based it on this script) to give automatic measurements of segmented sounds in a TextGrid. It is an updated version of the “msr&check…” file that I made available along with the workshop I linked to above. At the time, I had recorded several wordlists in Pnar, and I spent countless hours segmenting the sounds in each word. My thinking was that even if my segmentation wasn’t precise, the sheer number of sounds and their tabulation would allow me to run valid quantitative analyses. As it worked out, this was mostly the case, and I was able to target the outliers for closer examination. I also got better at recognizing Pnar sounds from all the time I spent with the words. I have now updated this script to work nicely with the following script, which plots vowels for you in the Praat picture window, which can produce print-publication-friendly images.
Vowel plot for formants:
Another that I wrote/modified from other bits takes a comma-delimited CSV spreadsheet with formant values and plots them (in the standard vowel chart format) as a Praat drawing with an oval marking their standard deviation (“draw_formants_plot_std_dev.praat”). I wrote this primarily to produce a clearer image than the one produced by JPlotFormants for my PhD thesis. Thanks also to the Praat User Group for their help with getting the script right.
I recently modified this script to work nicely with the automatic measurement script above. What this means is that you can segment all your words using TextGrids, run the script above to produce a CSV, and then just run this script to plot characters from that CSV. I implemented a ‘Sequential’ option for the plot so you can plot one vowel at a time, which means that you can leave all the segmented consonants (and VOT annotations) in the CSV file for later analysis. Or you can remove them, up to you. Just keep in mind that if you do have consonants in the CSV, it WILL try to plot them on the chart unless you choose the Sequential option.
The third script linked here (“tone_analysis.praat”) I recently wrote in order to take continuous measurements of tones without normalization. This is more for exploration of tonal systems on a per-speaker basis, allowing the investigator to identify whether length is potentially a factor in the characteristics of a particular tone. I am planning to modify it to allow for percentage-based analysis (and thus normalization) of tones, which could be used by the investigator to create clearer plots once they identify the characteristics of the individual tones. But I haven’t gotten around to it yet. I’ll write another blog post when I do.
As a final note, these scripts are really just the tip of the iceberg when it comes to the kind of analysis you can do in Praat. For more on Praat scripting, check out this great tutorial, Will Styler’s excellent blog, the scripts he uses/maintains, these resources at UW and these from UCLA. You can also follow along with Bartlomiej Plichta as he leads you through some scripting lessons in his videos, which are very useful.