Acoustic measurements on
prosody using Praat
Bert Remijsen
Universiteit Leiden & University of Edinburgh
1
Overview
Brief motivation
Introduction to Praat scripting
Measurement of –
> Vowel quality
> Fundamental frequency
> Voice quality and intensity
2
Overview
Topics in relation to measurements:
[>
>
>
>
>
Data collection and processing]
How to measure it in Praat
(Semi-)automating measurements
Displaying the descriptive statistics
Inferential statistics
3
Motivation
4
Motivation
Why quantitative analysis of prosody?
> quantitative results can be used to test
hypotheses
5
Motivation
Why quantitative analysis of prosody?
> humans are bad at determining the acoustic
cause of prosodic variation by ear
E.g.:
- controversy on lexical stress
- perception of pitch-accent
6
Motivation
Why quantitative analysis of prosody?
> Prosodic contrasts are often realized in terms
‘packages’ of prosodic correlates.
E.g.: stress: duration, vowel quality, intensity
complementary quantity: duration, vowel q.
pitch-accent: fundamental frequency (f0),
duration, etc.
7
Motivation
Why quantitative analysis of prosody with
Praat?
> Allows for measurement, manipulation, and
representation of the full range of
acoustic parameters.
> Relatively easy to (semi-)automate procedures
by means of scripts.
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How to write a Praat script?
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How to write a Praat script?
A. Try to start out from an existing script
> For example, check on:
http://uk.groups.yahoo.com/group/praat-users
> Praatscripts introduced in this presentation can
be found at:
http://www.ling.ed.ac.uk/~bert/praatscripts
10
How to write a Praat script?
B. Writing (part of) a script from scratch
> Do the steps by hand for one item
> Display them using Paste history
> Combine these steps with control structures,
guided by the manual.
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How to write a Praat script?
An annotated script:
Script: msr_duration.psc
Function: collecting durations for onset, nucleus
and coda of a target word, for each file in list.
Automatic.
12
How to write a Praat script?
Common components:
> User interface (form … endform)
> Getting the input files (Read …)
> Finding point of measurement (using TextGrid)
> Measurements
> Writing output to file (e.g. fappendinfo)
13
How to write a Praat script?
The dataset:
> One long sound file – e.g. the whole recording
session, with information on sections in the
TextGrid.
> One item-per-file. If so, it is best to encode as
much useful information as possible in the
filename, in a structured way.
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How to write a Praat script?
> One item-per-file. If so, it is best to encode as
much useful information as possible in the
filename, preferably fixed-width.
E.g.:
dataset_code
d2_2_012_s_1
speaker_no
item_no
repetition_no
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s(ingular) / p(lural) [S&R]
How to write a Praat script?
Reasons:
> Saves work coding in statistics package
> The fields in the name can be searched with a
Praat script (using string pattern matching).
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How to write a Praat script?
Script: openlist.psc
Function: open specific objects associated with
item in list
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How to write a Praat script?
Script: openlist_specificitem.psc
Function: This script searches on the item code –
the third field in the name.
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Measuring vowel quality
19
Vowel quality
Measurement in Praat
How to measure formants in Praat?
I.
The point of measurement
II. An algorithm and a protocol
III. Semi-automating measurements
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Vowel quality
Measurement in Praat
I. The point of measurement – possibilities:
> Where F1 reaches its maximum
> Small domain centered on temporal mid point
> Averaged over (middle x% of) vowel.
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Vowel quality
Measurement in Praat
II. An algorithm and a protocol
1. Produce Formant object using default algorithm
(Burg) and parameters (5 formants below 5000
Hz [male] / 5500 Hz [female])
2. Track using default values (male values =
female values – 10 %).
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Vowel quality
Measurement in Praat
3. Protocol for when the value is incorrect:
E.g.: weak F2 of high back vowels often
missed; F3 reported as F2
Options:
- Use LPC with more coefficients
- Retrack with changed F1/2 ref.
The strategy is to be fixed within a single study.
23
Vowel quality
Measurement in Praat
III. Semi-automating the measurements
> Formant measurements should be checked. A
fully-automated procedure is not an option.
> Instead: automate all the repetitive actions.
24
Vowel quality
Measurement in Praat
Script: msr&check_f1f2_indiv_interv.psc
Function: Makes measurement as proposed above,
Point of measurement: midpoint of an
interval – suitable for analysis for
monophthongs.
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Vowel quality
Measurement in Praat
Script: msr&check_f1f2_indiv_point.psc
Function: Makes measurement as proposed above,
Point of measurement: points on a point tier
– suitable for analysis of di/triphthongs.
> These scripts can easily be modified to process
a batch in one go – still with check.
26
Vowel quality
Scaling
The formant values, once collected, can be scaled
in a number of ways:
1. Individual frequencies or frequency differences?
> Vowel height: F1-F0 or F1
> Advancement: F2-F1 or F2
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Vowel quality
Scaling
The formant values, once collected, can be scaled
in a number of ways:
2. F1 x F2, or others formants as well?
> F1 x F2
> F0 x F1 x F2 x F3
28
Vowel quality
Scaling
The formant values, once collected, can be scaled
in a number of ways:
3. Acoustic / psycho-perceptual scale?
> hertz (Hz)
> Logarithmic (ST)
> MEL
> ERB
> Bark
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Vowel quality
Scaling
The formant values, once collected, can be scaled
in a number of ways:
4. Cross-speaker comparisons?
> z-transformation (Lobanov)
> Gerstman
> Constant Log Interval Hypothesis
30
Vowel quality
Scaling
Ideal set-up for normalization (Adank 2003):
> Individual frequencies rather than Δ’s
> hertz (Hz) rather psycho-acoustic scale
> No need to consider F0 and F3
> between-speaker variation: z-transformation
31
Vowel quality
Analysis / vowel plots
The formant values, can be interpreted best in a
vowel plot (F1 x F2).
Characteristics of a good vowel plot:
> Inverted axes
> Over speakers (so z-transformed)
> Categories labeled using IPA
32
Vowel quality
Analysis / vowel plots
The formant values, can be interpreted best in a
vowel plot (F1 x F2).
Characteristics of a good vowel plot:
> Inverted axes
> Over speakers (so z-transformed)
> Categories labeled using IPA
Praat can do it.
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Vowel quality
Analysis / vowel plots
Example:
- The vowels of
Dinka: /i,e,,a,,o,u/
- Ellipses encircle 1
standard deviation
- Separate ellipses
for compl. quantity
- Values averaged
over 2 repetitions of
36 items uttered by
5 speakers.
-1
iC
iCC
uC
CC
u
eC
oCC
eCC
0
CCC

oC
C
CC
1
aCC
2
3
2
aC
1
0
-1
F2 (z-transformed)
-2
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Vowel quality
Analysis / vowel plots
Example:
- The vowels of
Dinka: /i,e,,a,,o,u/
- Ellipses encircle 1
st. dev. (68%)
- Separate ellipses
for compl. quantity
- Values averaged
over 2 repetitions of
36 items uttered by
5 speakers.
-1
iC
iCC
uC
CC
u
eC
oCC
eCC
0
CCC

oC
C
CC
1
aCC
2
3
2
aC
1
0
-1
F2 (z-transformed)
-2
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Vowel quality
Analysis / vowel plots
1. Create a TableofReal, with, for each token:
> praat-code for the IPA label (e.g. ‘’ is ‘\ep’)
> z-transformed F1 and F2; sign inverted (I do
this in SPSS)
> Header contains axis labels and no. of tokens
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Vowel quality
Analysis / vowel plots
Example: formants_tor.txt:
File type = "ooTextFile"
Object class = "TableOfReal"
numberOfColumns = 2
columnLabels []:
"F2 (z-transformed)"
numberOfRows = 341
"F1 (z-transformed)"
row [1]: "i^C" -1.6595 1.2794
row [2]: "i^C" -1.9973 1.2538
…
row [341]: "o^C" 0.6245 0.0380
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Vowel quality
Analysis / vowel plots
2. Open the TableofReal in Praat, and use either:
> Draw scatter plot
to plot individual values; each token is
marked by its (IPA) label.
or
> Draw sigma ellipses
ellipses, sized by user in terms of st.
devs. (sigma). (IPA) label plotted at
center.
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Vowel quality
Analysis / vowel plots
Either way, plot with no for Garnish and
Discriminant plane
3. In Picture window, add marks on x and y axes,
inverting the inverted sign back to normal – for
example:
One mark left... -2 no yes no 2
This gives a y-axis mark in terms of z-scores of
‘2’ at -2 on the y-axis, without plotting ‘-2’.
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Vowel quality
Analysis / inferential tests
Characteristic inferential test: ANOVA
> within-subjects
> multivariate (dependents zF1 and zF2)
> factor(s) vowel quality (and e.g. lexical stress /
intonational accent / position in phrase / etc.).
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Measuring fundamental
frequency
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F0
Overview
> Issues in measuring F0
> Scaling
> Descriptive stats
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F0
Issues in measuring F0
I. For detailed study about the realization of tonal
contrasts, consonants in target words should be:
+
nasals
liquids
approximants, rhotics
voiced fricatives
–
unvoiced fricatives, stops
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F0
Issues in measuring F0
BUT: other may be more important – such as the
availability of minimal-set data:
/ba1/
/ba3/
/ba121/
/ba12[p]/
/ba41/
/ba21/
/ ba31/
Low level
High level
Rise-fall
Low Rise
Extra High Fall
Low Fall
High Fall
‘to remain’
‘ancestor’
‘stiff’
‘father’
‘to hit’
‘to blow’
‘when’
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F0
Issues in measuring F0
II. F0 measurements need to be checked for
octave jumps etc.
> suggestion: use a semi-automated procedure
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F0
Issues in measuring F0
Script: lst2f0&check.psc
Function: This script automates all the repetitive
actions involved in the checking of F0 tracks. It
calculates the F0 track (Pitch object), plots it in
the Picture window, gives the opportunity to fix
errors if need be, and then writes the (fixed)
Pitch object to a file. Batch processing using file
list.
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F0
Issues in measuring F0
III. The point of measurement – turning points can
be determined:
> by eye
> using mathematical modelling. MOMEL (Hirst
& Espesser) is implemented in Praat. See
also recent work by Grabe & Kochanski.
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F0
Issues in measuring F0
Script: momel_modif.psc
Function: Praat implementation of the MOMEL
algorithm. (Original implementation in the MES
signal processing package)
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F0
Scaling
From physical F0 trace to psycho-acoustic track.
1. Normalization for the logarithmic nature of
pitch perception:
> hertz (Hz)
> semitone (ST)
> Equivalent Rectangular Bandwidth (ERB)
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F0
Scaling
From physical f0-track to psycho-acoustic track.
1. Normalization for the logarithmic nature of
pitch perception:
> hertz (Hz)
> semitone (ST)
> Equivalent Rectangular Bandwidth (ERB)
Latest news: semitone is best (Nolan 2003).
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F0
Scaling
2. Normalization across speakers:
> No need to normalize for slope differences
expressed in ERB or ST.
> Absolute values can be normalized using the ztransformation.
51
F0
Analysis / Plotting tracks
How to interpret the data, and communicate
tendencies to others? The problem:
> Averages of F0 measures expressed as numbers
in Hz are hard to interpret. ST, ERB and zscores are even harder to interpret.
> Visual illustration by means of F0 tracks of
individual cases fail to exploit the dataset.
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F0
Analysis / Plotting tracks
The solution:
> Represent F0 visually across speakers, by
means of tracks normalized for time.
> I.e.: graph used as a descriptive stat (reports
average)
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F0
Analysis / Plotting tracks
Example 1:
- The 6 lexical tones of
Matbat
- Normalized time
- Utterance-medial
position, following
low target
- Tracks averaged over
2 repetitions of 48
items uttered by 8
speakers. (784 tokens)
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F0
Analysis / Plotting tracks
Example 2:
- The 3 word-prosodic
patterns of Papiamentu.
- Normalized time
- Whole sentence
represented.
- Tracks averaged over
2 repetitions of 2 items
uttered by 8 speakers.
(96 tokens)
SUBJ COP O1 V1 O2 V2 PREP.
word-acc. I, penult. stress
word-acc. II, penult. stress
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word-acc. II, final stress
F0
Analysis / Plotting tracks
Script: pp_show_series10.psc (example)
Function: on the basis of checked tracks, the
scripts produces a text file with an F0 values for
each of 8 points of measurement. Takes voicing
at edges into consideration.
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Measuring overall / selective
intensity (dB)
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dB
Introduction
Variation in perceived voice quality (breathy,
modal, creaky) correlates with distribution of
energy in spectrum.
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dB
Introduction
Functions include:
1. Utterance-level contrasts
Example: creaky voice correlates with low F0 –
Q:
A:
The slugs ate the dahlias, didn’t they?
No, that’s not true / the rabbits ate the dahlias,
not the slugs.
(Thanks to Mariko Sugahara for the example)
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dB
Introduction
2. Word-level contrasts – on its own…
Dinka example – breathy vs. modal
raal
raall
ltt
‘vein-sg.’
‘vein-pl.’
lt
‘insult-sg.’ ‘insult-pl.’
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dB
Introduction
Functions include:
2. Word-level contrasts – on its own…
Dinka example – breathy vs. modal
raal
raall
ltt
‘vein-sg.’
‘vein-pl.’
lt
‘insult-sg.’ ‘insult-pl.’
… or as a package (register tone – e.g. MonKhmer languages, Chamic languages)
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dB
Introduction
Variation in perceived loudness correlates with:
> the distribution of energy in the spectrum
(spectral balance)
> overall intensity
Functions include:
> Lexical stress (cf. Sluijter & van Heuven 1996)
> Phrasal accent (cf. Heldner 2003)
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dB
Introduction
In summary:
> Selective intensity marks distinction in voice
quality AND distinctions in loudness.
> Loudness contrasts may also correlate with
overall intensity.
> It remains unclear whether / to what extent
loudness and voice quality have separate
correlates.
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dB
Measuring overall intensity
> No need for checking. Automated procedure is
possible, cf. measurement of duration.
> Important issue: controlling for variation in
irrelevant factors in the course of session.
> Relate intensity of target segment to the
intensity of (part of) the carrier utterance.
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dB
Measuring selective intensity
Abundance of possible measurements, including:
> H1-H2
See thematic issue of
JPhon 29:4 (2001)
> H1-A1, H1-A2, H1-A3
> Dynamic filter (Heldner)
> Average within a range (Sluijter & van Heuven)
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dB
Measuring selective intensity
Recommendations:
> For detailed acoustic study: a measure of
specific spectral properties is best
(explanatory adequacy).
> In relation to a big corpus, Heldner’s filterbased measure seems best (relatively vowelindependent; easy to automate)
> Try out several / Make your own variation
66
dB
Measuring selective intensity
How to:
> Point of measurement (cf. vowel quality)
> Semi-automating measurements using script
67
dB
Measuring selective intensity
Script: msr&check_spectr_indiv_interv.psc
Goal: Semi-automated procedure for measurement
of H1, H2, A1, A2, A3. Extension of vowel
quality script.
68
Acknowledgements
> The organizers, for inviting me.
> Thanks to Patti Adank and Alice Turk,
for discussions on measurements of
vowel quality; and to Helen Hanson,
for discussions on voice quality.
> The Netherlands Organization for
Scientific Research (NWO), for
funding my research by means of a
postdoc grant to Vincent van Heuven.
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Conference announcement:
Between Stress and Tone
Topic:
When/where:
Abstracts due:
Details:
Typology of prosodic systems
16-18 of June 2005 / Leiden
1 of November 2004
http://www.iias.nl/iias/agenda/best/
70