Post on 17-Dec-2015
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Acoustic measurements on prosody using Praat
Bert RemijsenUniversiteit Leiden & University of Edinburgh
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Overview
Brief motivation
Introduction to Praat scripting
Measurement of –
> Vowel quality
> Fundamental frequency
> Voice quality and intensity
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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
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Motivation
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Motivation
Why quantitative analysis of prosody?
> quantitative results can be used to test hypotheses
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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
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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.
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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
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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.
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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)
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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
s(ingular) / p(lural) [S&R]
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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
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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.
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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.
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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.
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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
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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
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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
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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
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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.
C
CCCCC
aC
aCC
eC
eCC
iC
iCC
oCoCC
uC
uCC
00
3
2
1
-1
-22 1 -10F2 (z-transformed)
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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.
C
CCCCC
aC
aCC
eC
eCC
iC
iCC
oCoCC
uC
uCC
00
3
2
1
-1
-22 1 -10F2 (z-transformed)
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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)" "F1 (z-transformed)"
numberOfRows = 341
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/ Low level ‘to remain’
/ba3/ High level ‘ancestor’
/ba121/ Rise-fall ‘stiff’
/ba12[p]/ Low Rise ‘father’
/ba41/ Extra High Fall ‘to hit’
/ba21/ Low Fall ‘to blow’
/ ba31/ High Fall ‘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 z-transformation.
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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
z- scores 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
word-acc. II, final stress
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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: The slugs ate the dahlias, didn’t they?
A: 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
raal raall ltt lt
‘vein-sg.’ ‘vein-pl.’ ‘insult-sg.’ ‘insult-pl.’
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dB Introduction
Functions include:
2. Word-level contrasts – on its own…
Dinka example – breathy vs. modal
raal raall ltt lt
‘vein-sg.’ ‘vein-pl.’ ‘insult-sg.’ ‘insult-pl.’
… or as a package (register tone – e.g. Mon-Khmer 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
> H1-A1, H1-A2, H1-A3
> Dynamic filter (Heldner)
> Average within a range (Sluijter & van Heuven)
See thematic issue of JPhon 29:4 (2001)
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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 filter-based measure seems best (relatively vowel-independent; easy to automate)
> Try out several / Make your own variation
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dB Measuring selective intensity
How to:
> Point of measurement (cf. vowel quality)
> Semi-automating measurements using script
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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.
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> 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.
Acknowledgements
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Conference announcement:
Between Stress and Tone
Topic: Typology of prosodic systems When/where: 16-18 of June 2005 / Leiden Abstracts due: 1 of November 2004 Details: http://www.iias.nl/iias/agenda/best/