Deadline extended! See https://magnuson.psy.uconn.edu/Positions
Jim Magnuson and Jay Rueckl are looking for a postdoc to work on computational modeling of human speech recognition. We are working on bridging the gap between cognitive models and state-of-the-art deep learning models for automatic speech recognition. Details available here: https://magnuson.psy.uconn.edu/Positions/
We had 3 presentations/papers at CogSci2019.
- Magnuson, J.S., Li, M., Luthra, S., You, H., & Steiner, R. (2019). Does predictive processing imply predictive coding in models of spoken word recognition? In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 735-740). Montreal, QB: Cognitive Science Society. [PDF]
- Magnuson, J.S., You, H., Rueckl, J. R., Allopenna, P. D., Li, M., Luthra, S., Steiner, R., Nam, H., Escabi, M., Brown, K., Theodore, R., & Monto, N. (2019). EARSHOT: A minimal network model of human speech recognition that operates on real speech. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 2248-2253). Montreal, QB: Cognitive Science Society. [PDF]
- McClelland, J.L., McRae, K., Borovsky, A., Kuperberg, G., & Hill, F. (2019). Symposium in memory of Jeff Elman: Language learning, prediction, and temporal dynamics. In A.K. Goel, C.M. Seifert, & C. Freksa (Eds.), Proceedings of the 41st Annual Conference of the Cognitive Science Society (pp. 33-34). Montreal, QB: Cognitive Science Society. [PDF]
After years of dedicated work, Monica Li (with support from her co-authors) has published a terrific new paper in the Journal of Memory & Language:
Li, M.Y.C., Braze, D., Kukona, A., Johns, C.L., Tabor, W., Van Dyke, J. A., Mencl, W.E., Shankweiler, D.P., Pugh, K.R., & Magnuson, J.S. (2019). Individual differences in subphonemic sensitivity and phonological skills. Journal of Memory & Language, 107, 195-215. https://doi.org/10.1016/j.jml.2019.03.008 (links at publications page)
In addition to an epic set of experiments and individual differences measures (and implications for whether phonological processing is unusually precise or imprecise in individuals with lower reading ability), Monica provides a direct comparison between growth curve analysis (GCA) and generalized additive models (GAMs).
Okay, this one is actually new — it just appeared online today.
You, H. & Magnuson, J. S. (2018). TISK 1.0: An easy-to-use Python implementation of the time-invariant string kernel model of spoken word recognition. Behavior Research Methods. doi:10.3758/s13428-017-1012-5 [PDF]
This documents Heejo You’s beautiful re-implementation of Thomas Hannagan’s original TISK code. We were sad that Thomas could not join us in this paper (he has a new job in industry that precluded that), but we are immensely grateful to him for his help and advice.
I am very pleased to announce (belatedly) that the lab has a new paper out in Frontiers:
Magnuson, J. S., Mirman, D., Luthra, S., Strauss, T., & Harris, H. (2018). Interaction in spoken word recognition models: Feedback helps. Frontiers in Psychology, 9:369. doi:10.3389/fpsyg.2018.00369 [HTML]
This paper was a very long time in the making. This project inspired the jTRACE re-implementation of TRACE, and previous attempts at publication were stymied. The upshot of the paper is that feedback in a model like TRACE affords graceful degradation in the face of noise.
Randomize duration and F0 of speech files in Praat
Summary: I describe a script that makes random modifications to duration and pitch. It can serve as an example for how to read in all .wav files in a directory and modify them from the command line.
For a machine learning project, we wanted to induce variability in a set of speech files without recording more tokens of our critical words. We thought, “let’s just use a Praat script to randomly jigger duration and F0 — that can’t be hard, can it?” Well, it is harder than I thought given that I don’t really grok Praat scripting — it is the most foreign and strange programming language I’ve ever encountered.
After consulting with various colleagues, I remembered that Sergey Kornilov had used a script to compress lots of files for a project. He sent me his script. I found a script by Shigeto Kawahara of Keio U. in Japan that modified pitch. Putting them together, I now have a script that is called from the command line and reads in all .wav files in a directory (the directory where the script is located), randomly compresses/expands the file according to a hard-coded range (i.e., you have to change the values in the file), randomly adjusts pitch (by multiplying by a scalar), and writes the resulting file in a folder called
output that must exist in the starting directory. The file names include tags indicating how duration and pitch were modified. Here’s the script. I hope someone else finds it helpful! You can copy the code and paste it into a text document that you might call something like
— jim magnuson
# 2017.11.17, Jim Magnuson, based on a compression script by
# Sergey Kornilov and
a pitch modification script by
# Shigeto KAWAHARA of Keio U.
# kludgy script to shift pitch by random factor between
# minF0 and maxF0
and compress/expand by random factor between
# minComp and maxComp.
# It does this for every .wav file in the directory where the
script is located, and writes
resulting files in ./output
# (so you must create a folder called output; note
# that the script will overwrite files in that directory
# without warning!).
# The output files have _dur_X and _f0_Y inserted in their
# names. For example,
_dur_87 means compression to factor of
# 0.87, _dur_108 means compression
(expansion) to factor of
# 1.08, etc.
# set locations
inputDir$ = "./"
outputDir$ = "./output/"
# set ranges for pitch and compression
minF0 = 0.5
maxF0 = 2.0
minComp = 0.5
maxComp = 2.0
# used to incorporate duration and F0 changes
# in out file name
resolution = 100
# read *.wav filenames into strings
strings = Create Strings as file list: "list", inputDir$ + "/*.wav"
numberOfFiles = Get number of strings
# now loop through .wav list
for ifile to numberOfFiles
# open file in position ifile in string list
filename$ = Get string... ifile
# give a little info on the console re: progress
appendInfoLine: "Working on " + inputDir$ + filename$
# read the actual file
Read from file: inputDir$ + filename$
# set random duration factor
duration_scalar = randomUniform(minComp, maxComp)
# LENGTHEN AND RESYTHESIZE IN ONE STEP USING PSOLA
# 75 and 600 are standard parameters for the
# frequency range (hz)
used in periodicity analysis;
# the last argument is the compression factor
Lengthen (PSOLA)... 75 600 duration_scalar
# RANDOMIZING F0
# this is going to require Manipulation commands,
# so we need to
select the sound; let's name it
this_sound$ = selected$ ("Sound")
# grab it + give params for onset of analysis window and
# frequency range used for periodicty analysis
To Manipulation... 0.01 75 600
# pop pitch tier into memory (apparently)
Extract pitch tier
# set random pitch change factor
pitch_scalar = randomUniform(minF0, maxF0)
# modify pitch of the pitch tier in memory
Formula... self * pitch_scalar;
# reselect the sound and replace pitch tier
select Manipulation 'this_sound$'
plus PitchTier 'this_sound$'
Replace pitch tier
# reselect the sound??? and resythesize
select Manipulation 'this_sound$'
Get resynthesis (PSOLA)
# SAVE RESULTING FILES
# create rounded versions of the pitch and
# duration factors for use in filename
pitch_scalar_rounded = round(pitch_scalar * resolution)
duration_scalar_rounded = round(duration_scalar * resolution)
filenameDur$ = " 'filename$'" - ".wav" + "_dur" + "_" + string$(duration_scalar_rounded) + "_f0" + "_" + string$(pitch_scalar_rounded) + ".wav"
# update user and write to file
appendInfoLine: "* writing to " + outputDir$ + filenameDur$
Write to WAV file: outputDir$ + filenameDur$
select Strings list
# end of script