Linguistic 632
Articulatory Phonology
Fall 2008
Exercise 1: Task-dynamics Model
Purpose: to become familiar with the conceptual components of the task-dynamics model of speech
gestures as well as its implementation in the TADA application.
Technique: Generate the gestural score for selected English utterances automatically and manipulate the
dynamical parameter of the gestures and their activation intervals to demonstrate the phenomenon of
undershoot.
Procedure:
A. Generate a gestural score for English word “ten” and run the task-dynamic model
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Launch TADA. To do so in the Phonetics Lab, Open Parallels, and click the run button to start
the Windows emulation. There will be a shortcut to TADA on the desktop, and on the taskbar.
Double-click to launch.
From the GEST menu, select RUN. In the resultin output file and working directory dialogue,
navigate to the your Windows folder, which is in the Ling 632 folder (there is a shortcut to Ling
632). Then enter an alphanumeric id in the File name field. The output files generated by TADA,
which will contain a coupling graph for this utterance, will automatically be named TV<id>.o
and PH<id>.o. For example, if you enter ‘ten’ as the <id>, the files will be called TVten.o and
PHten.O.
Another dialogue entitled GEST input output specification will appear. In the Input text input
box (top field), enter an alphanumeric string. It will be automatically populated with the id which
you entered in step 2, as will the second Output field. Ignore the remaining two fields on this
dialogue for this exercise. Close the dialogue by clicking the [OK] button to save your input.
Click the [Open] button at the upper right of the main TADA GUI, and select the file TVten.O
which you just created.
This file will take a little time to open. After about 30-45 seconds, you should see the gestural
score displayed on the main TADA GUI. Click in the activation box corresponding to each
gesture, look in the Numerical Read-out panel at the values of the dynamical parameters and
activation interval, and make sure you know what each is gesture is and how to interpret the value
of the dynamical parameters. Click the [CD] and [CL] radio buttons and look at the parameter
values and make sure you understand what those are. Where two gestures of the same tract
variable overlap, make sure you understand which parameters go with which gestures.
Click the [TV] button, which will run the task dynamics model and generate tract variable
articulator time function.
The audio signal corresponding to this utterance is displayed in the top window, and the portion
of the signal which is currently selected is shown covered by a grey bar. Grab this selection bar
by clicking close to the right edge, then slide the edge to the left so that it no longer covers the
whole signal. You can now slide the box to choose different sections of the signals to display, and
shrink or expand the selection by manipulating either edge. Move the box so that its left edge is in
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the silent region of the /t/. (NOTE: the task-dynamic simulation always begins with the vocal tract
in position for schwa. This means that all utterances will begin with a vowel which will appear in
the interval before the first gesture reaches its target).
Click in the second panel to place a vertical cursor over the simulation, and examine the regions
in which the CD tract variable values are very low. These should be constriction intervals. Make
sure that this is consistent with the generated (pseudo-)acoustics and with the vocal tract displays
on the left.
Click the [Sound] button. You will hear the changing resonances of the vocal tract, but not the
word ‘ten,’ as this output has no source or nasalization control.
To generate the proper acoustic output for this simulation, launch the HLsyn application (from
the shortcut on the desktop or the taskbar). In HLSyn, click the [New] button at the top left of the
GUI. From the File menu, select Import > HL Parameters. Navigate to the folder in which you
are working and select the file <ten.hl>. Click [Synthesize] and then [Play] to hear the synthetic
waveform.
To output the synthetic waveform to TADA, select Export > Waveform from the HLsyn File
menu and navigate to the directory which contains your TADA files. Name the file the the id
<ten>. Return to the TADA GUI, click the [Signal] button and open the file <ten.wav> from the
wav, pcm files dialogue which appears. The acoustic signal which you generated using HLSyn
will now replace the pseudo-acoustics file, and you should hear it when you click [Sound].
Click on the [>] button in the Action panel, to animate the vocal tract displays and sound.
Save the gestural score by clicking on the [Save As] button. A TV Files dialogue will appear. In
the File name text input box, enter an alphanumeric <id> for the gestural score file, for example
ten. The gestural score will then be saved in the file <TVten.G>.
Choose another English word, preferably one with a CVC structure in which the initial C is an
obstruent, and repeat steps 1-13.
B. Examine the effect of reducing the frequency of the [TT clo] gesture of the /t/
1. Go to the Options menu and ensure that the Apply2otherTVs option is selected.
2. Open the file you saved in Step 14 <TVten.G> by clicking the [Open] button (top right, main
TADA GUI) and selecting it. Click the [TV] button to generate the time functions, then click the
[Signal] button to open the audio file <ten.wav>
3. Open a second TADA window by clicking the [New] button. Open the file <TVten.G> again in
the new window.
4. Find the TT closure gesture associated with initial /t/, and make sure the [CD] radio button is
selected. When you click in gesture box, you should see its parameters in the Read-out panel. It
should have a target (x0) for the TTCD of -2 (mm) and a frequency (f) of 8 (Hz). Replace the
value 8 with 4, and press Enter on the keyboard to save the change.
5. Select the [CL] radio button and make sure it has also changed to 4 Hz (this should have
happened automatically).
6. Repeat steps A.6-A.14 to generate sound and time functions for this new edited gestural score,
and to save them. Give the new files the id ten_f4.
7. Compare the audio and dynamic behaviour of the two versions. Make sure you understand the
cause of any differences.
8. Repeat steps 2-7 with the other word you generated.
C. Examine the effect of shrinking the activation intervals and their relative timing
1. Open the gestural score file <TVten.g>.
2. Make sure that the Apply2otherTVs option is selected in the Options menu, and that the
Proportional Freq to Act Interval is not checked.
3. What we want to do is to shrink the activation intervals and the relative timing of all gestures by
one half. To do so, first select one of the gestures that begins at frame 0 by shift-clicking on it.
Then go to the Read-out panel and decrease the end frame of the gesture by half. The (white)
gestural activation will change (though blue selection box will not). Click elsewhere in the TADA
display to release the gesture. Do all the rest of the gestures that begin at frame 0 in the same way.
4. Select on each gesture that begins later than Frame 0 and decrease both the Beg and End frame
values by one half.
5. Save the gestural score file to one with the <id> ten_short.
6. Generate the TV functions and sound (using HLsyn), as above. Compare the original ‘ten’ with
the one just generated. Does it still have a /t/ closure? Nasalization? You can also compare the
waveforms and spectrograms in Praat, to get a better look at, and listen to, the differences. To use
Praat, copy your files to your folder in Windows Share folder. There should be a shortcut to this
on the Windows desktop, and you can also access the folder from the MacOSX. Open the files
using MacOSX Praat.
7. Now click on each gesture and increase its frequency by a factor of 2. Save the resulting gestural
score with the <id> ten_short_fast.
8. Generate the TV functions and sound (using HLsyn), as above. Compare the original ten,
ten_short, and ten_short_fast. Does increasing the frequency improve the output in ten_short_fast
compared to ten_short?
9. Perform this same shortening and then frequency shift to the other word you generated. Compare
the results with that from ten.