Christopher Carignan, Wei-rong Chen, Muawiyath Shujau, Catherine T. Best, and I recently published an article about our new 3D-printable ultrasound transducer stabilizer. The system stabilizes an ultrasound transducer submentally (under the chin) to allow consistent imaging of the tongue during speech. The provided materials work for midsaggital imaging of the tongue using a few select ultrasound transducers like the Logiq E 8C-RS and the telemed transducers for Articulate Instruments systems, but can be modified easily to allow for other probes, or for coronal imaging.
The system costs about $400 NZD in materials to produce, making it quite affordable. It is also very comfortable compared to most stabilization systems, and is accurate to within about 2mm of motion in any direction, and 2 degrees of rotation in any direction. More details can be found in the article documenting the system.
Here is an image of the system, fully assembled and worn:
This work is therefore in part a follow-up to some of my co-authored research into biomechanical modelling of English /ɹ/ variants, indicating that vocalic context influences variation through muscle stress, strain, and displacement. It is, by these three measures, “easier” to move from an /i/ to a tip-down /ɹ/ , but from /a/ to a tip-up /ɹ/.
In this study, for speakers who vary at all (some only do tip-up or tip-down), they are most likely to produce tip-up /ɹ/ in these conditions:
back vowel > low central vowel > high front vowel
initial /ɹ/ > intervocalic /ɹ/ > following a coronal (“dr”) > following a velar (“cr”)
The results show that allophonic variation of NZE /ɹ/ is similar to that in American English, indicating that the variation is caused by similar constraints. The results support theories of locally optimized modular speech motor control, and a mechanical model of rhotic variation.
The abstract is repeated below, with links to articles contained within:
This paper investigates the articulation of approximant /ɹ/ in New Zealand English (NZE), and tests whether the patterns documented for rhotic varieties of English hold in a non- rhotic dialect. Midsagittal ultrasound data for 62 speakers producing 13 tokens of /ɹ/ in various phonetic environments were categorized according to the taxonomy by Delattre & Freeman (1968), and semi-automatically traced and quantified using the AAA software (Articulate Instruments Ltd. 2012) and a Modified Curvature Index (MCI; Dawson, Tiede & Whalen 2016). Twenty-five NZE speakers produced tip-down /ɹ/ exclusively, 12 tip-up /ɹ/ exclusively, and 25 produced both, partially depending on context. Those speakers who produced both variants used the most tip-down /ɹ/ in front vowel contexts, the most tip- up /ɹ/ in back vowel contexts, and varying rates in low central vowel contexts. The NZE speakers produced tip-up /ɹ/ most often in word-initial position, followed by intervocalic, then coronal, and least often in velar contexts. The results indicate that the allophonic variation patterns of /ɹ/ in NZE are similar to those of American English (Mielke, Baker & Archangeli 2010, 2016). We show that MCI values can be used to facilitate /ɹ/ gesture classification; linear mixed-effects models fit on the MCI values of manually categorized tongue contours show significant differences between all but two of Delattre & Freeman’s (1968) tongue types. Overall, the results support theories of modular speech motor control with articulation strategies evolving from local rather than global optimization processes, and a mechanical model of rhotic variation (see Stavness et al. 2012).
This is a guide to the use of ultrasound and EMA in combination. It is a bit out of date, and probably needs a day or two of work to make fully correct, but it describes the techniques I use with 3 researchers. Of course I wrote this years ago, and now I can run an Ultrasound/EMA experiment by myself if I need to.
This is a simple set of one-line scripts for capturing ultrasound audio and video.
I built it to work as batch files through the WINDOWS OS command-line because that’s the OS that seems to give me the highest frame-rate. (I use macs, and this works with the windows OS booted from bootcamp).
Look at the README file to make sure you use the scripts properly.
Here I offer you a program that will scan through all of the PRAAT textgrids in a folder, and for each it will search for the named textgrid tier. Then it will loop through each segment in that tier, find the ones with text in them, and cut clips from a video with the exact same base-name based on those time stamps. Each video will be cropped to the region given in the cropping variable (currently set for the Logiq E ultrasound).
The program uses R as a wrapper to load PRAAT textgrid files, uses a PERL program textgrid2csv.pl (copyright Theo Veenker <T.J.G.Veenker@uu.nl>) to make a CSV file usable in PRAAT, and work with that data.
Therefore: 1) You have to extract audio from the video file you want to crop and segment, and transcribe and label that video in a PRAAT textgrid to the detail you want to use for each cropped video file (usually a word or phrase). 2) Go into the code, and change all the variables at the top according to your needs.
Lots of work, but this program will still save you heaps of time. It is especially useful if you are using AAA for ultrasound analysis but only have video instead of AAA’s proprietary ultrasound file storage format.
Note, I provide sample data in the zip file to test the program – a swallow used for a palate trace. Get the program to run with the sample before you modify it for your own purposes.
Dealing with video files is just about the most obnoxious experience a researcher can have. I wasted a *year* of research getting this one wrong before I realized the only, and I do mean only, effective solution involves FFMPEG. Here I offer you a program that will re-align every video held in one directory and for which you have alignment data.
The program uses R as a wrapper to load a .csv file that contains the meta-data on a directory of video files that you want to align.
Therefore: 1) You have to hand-check the audio-visual offsets for each file, and put that into the .csv file. 2) You also have to make sure you have installed FFMPEG, SOX, PERL, R, and the R modules “reader” and “gdata”. 3) You have to look inside my R-code and change the paths and extensions to make the program will work on your computer.
I provide a sample video with a swallow used to obtain a palate trace. Get the software to work on your machine with this sample before modifying the code for your project.
Some time ago, I was on Radio New Zealand discussing my research on the use of air flow to enhance speech perception. Alas, it did not have the commercial value we thought it would have due to the need for higher airflow than is feasible necessary to enhance speech perception in continuous speech. However, it since led to the development of a mask-less and plate-less air flow estimation system that works well. The system provides useful biofeedback information that has the potential to help with speech therapy and accent modification.
I rewrote a PRAAT script – shamelessly edited from Mietta’s amazing original – but modified to work well on both MAC and PC. The scrip opens all the WAVE or AIFF files and matching textgrids, and take a look at the relevant tier (defaults to 3) to extract duration, f0 (pitch), F1, F2, F3, cog. The PRAAT script and readme file are located here.
Mietta Lenne’s scripts, for those who don’t know, seem to be on Github these days.
TreeForm is a cross-platform syntax tree drawing software written in Java. TreeForm has been freely available as an open-source project since February 2006, and updated to its current form in 2010. To download, please go to the TreeForm site on SourceForge.net. You can also watch the TreeForm video below.
In the spring of 2007, TreeForm was edited extensively and tested for suitability among a select group of linguists at UBC. A comparison of TreeForm along with other methods of drawing syntax trees, along with the cognitive walkthrough. I published the results with Daniel Archambault in Literary and Linguistic Computing as an article entitled TreeForm: Explaining and exploring grammar through syntax trees.
With TreeForm, you can create and reorder syntax trees with Unicode fonts, add features, case, associations and movement lines in black and white or color, and save the results for future editing or print to pdf for use in word processing or Latex documents. TreeForm itself has been used extensively by researchers all over the world. Special thanks to all those who have helped make TreeForm a successful project, to my many users, and to the 136,000+ who have tried TreeForm since I uploaded it to SorceForge.