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uchicago_beijing_creak_kmyu_201906.pdf

krisyu
June 14, 2019

 uchicago_beijing_creak_kmyu_201906.pdf

Language Diversity, Contact, and Change. June 14-16, 2019. University of Chicago Center in Beijing. Conference website: https://lucian.uchicago.edu/blogs/lpl/workshop/

Abstract: The phonetic realization of tonal contrasts in many languages involves not just fundamental frequency/pitch, but also phonation. For example, it is well known that Tone 3 in Beijing Mandarin (e.g., the word for horse) is often creaky, and that the falling ”g” tone in Hmong is known to be breathy. In this talk, I illustrate methods for studying the phonetics of phonation contrasts, with an emphasis on creak—from data collection to data analysis. I present empirical data on tonal contrasts involving creak in Cantonese, Hmong, and Beijing and Taiwan Mandarin. All of these languages and language varieties include tones that are both low in fundamental frequency/pitch and often creaky. But it is a mistake to think that this implies that there is a uniform phonetic characterization of the creaky tones across these languages and speakers of these languages. In fact, it is unclear that there is any single phonetic dimension that can effectively characterize the presence of creak in speech. Rather, there are phonetically distinct kinds of creak, although our understanding of these kinds of creak is still limited. Thus, in addition to discussing the phonetic characterization of the contrast between creaky and non-creaky phonation, I also discuss progress towards phonetic characterizations of distinct kinds of creak and what conditions variation in creak across speakers and languages in Cantonese, Hmong, and Beijing and Taiwan Mandarin.

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June 14, 2019
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  1. VARIATION IN THE PHONETICS OF
    CREAKY VOICE IN CANTONESE,
    HMONG, AND MANDARIN
    KRISTINE YU (余世宁)
    ASSOCIATE PROFESSOR, DEPARTMENT OF LINGUISTICS, U. MASSACHUSETTS AMHERST
    LANGUAGE DIVERSITY, CONTACT, AND CHANGE
    UCHICAGO BEIJING CENTER
    JUNE 14, 2019
    1

    View Slide

  2. UCHICAGO BEIJING: CREAK
    MANDARIN TONES ON 5-POINT PITCH SCALE
    2
    5
    4
    3
    2
    1
    Pitch scale
    妈 ma (55)
    ⿇麻 má (35)
    骂 mà (51)
    ⻢马 mǎ (213)
    High
    Low

    View Slide

  3. UCHICAGO BEIJING: CREAK
    MANDARIN TONES: F0 CONTOURS
    3
    Fundamental
    frequency (f0) is the
    primary acoustic
    correlate of pitch
    Xu (1997)
    妈 (55)
    ⿇麻 (35)
    ⻢马 (213)
    骂 (51)
    ⻢马 mǎ (213)
    ⿇麻 má (35)
    妈 ma (55)
    骂 mà (51)

    View Slide

  4. 4
    http://youtu.be/P2pLJfWUjc8?t=3m53s
    Fundamental frequency is the rate
    of vocal fold vibration

    View Slide

  5. UCHICAGO BEIJING: CREAK
    VOCAL FOLD MOVEMENT DURING A SINGLE PHONATION CYCLE
    5
    Kreiman & Sidtis (2011):
    Fig 2.12, p. 32
    P h o n a t i o n i s t h e
    process by which
    vocal folds modulate
    o u t g o i n g a i r t o
    produce sound.
    Different vocal fold
    c o n fi g u r a t i o n s i n
    phonation result in
    changes in perceived
    voice quality.
    Garellek (2019)
    Front view
    https://www.researchgate.net/
    figure/a-Frontal-view-of-the-
    human-larynx-b-Picture-of-the-
    larynx-during-phonation-c-
    during_fig1_5326043

    View Slide

  6. UCHICAGO BEIJING: CREAK
    MANDARIN TONES: CREAKY PHONATION (嘎裂化)
    6
    Xu (1997)
    ⻢马 21(3)
    ⻢马 mǎ, 21(3)
    especially
    associated with
    creak in phonation.
    Tone with lowest f0
    ⻢马 mǎ (213)
    ⿇麻 má (25)
    妈 ma (55)
    骂 mà (51)
    嘎裂化 (CREAK)

    View Slide

  7. UCHICAGO BEIJING: CREAK 7
    https://chinesepod.com/blog/2016/03/24/speak-mandarin-third-tone-like-a-kardashian/

    View Slide

  8. UCHICAGO BEIJING: CREAK 8
    Mandarin: Kuang (2017)+refs within; Cantonese: Yu & Lam (2014) + refs within; Hmong: Esposito (2012), Garellek et al. (2013) +
    refs within; creaky falling/dipping tone typology, “Contour-Unspecified Low Tone”: Zhu (2015, §2), Zhu et al. (2012).
    Mandarin Cantonese
    White
    Hmong
    Level 55 55, 33, 22 55, 33, 22
    Rise 35 25, 23 24
    High fall 51 42, 52
    (breathy contrast)
    Low fall 21 (T3) 21 (T4) 21 (m)
    CREAK IN LOW FALLING TONES ACROSS LANGUAGES
    creaky
    Lowest falling tone often produced with creak…

    View Slide

  9. UCHICAGO BEIJING: CREAK
    DISTINCT TYPES OF CREAK: CANTONESE 21
    9
    Time (s)
    0.8 0.9
    -0.4798
    0.3565
    0
    Narrow-band spectrograms Waveforms
    Time (s)
    1.09 1.19
    -0.1679
    0.2286
    0
    Time (s)
    0.7288 1.043
    0
    1000
    Frequency (Hz)
    0.728834954
    Time (s)
    1.055 1.276
    0
    1000
    Frequency (Hz)
    1.05537273 1.27621711
    Some
    type
    Another
    type
    …and in fact, with variable types of creak…
    jiu33 lau lau jak̚3

    View Slide

  10. UCHICAGO BEIJING: CREAK 10
    …BUT UNIFIED TRANSCRIPTION OF CREAK IN IPA
    * but see VoQS voice quality symbols extensions to IPA: https://doi.org/10.1017/S0025100317000159, and
    appendix
    Zhu (2015)

    View Slide

  11. UCHICAGO BEIJING: CREAK
    TALK OVERVIEW: LANGUAGE DIVERSITY
    11
    ▸ High-level, introductory overview of diversity in
    creaky voice within and across languages
    ▸ Examples from analysis of low falling tones in
    Cantonese, White Hmong, Beijing Mandarin, (Taiwan
    Mandarin)
    ▸ Idea: recognizing types of creaky voice can help us
    understand how variation in creaky voice is related to
    linguistic structure

    View Slide

  12. UCHICAGO BEIJING: CREAK
    PHONATION STUDIES IN LANGUAGES OF CHINA
    12
    Kong & Wang (2013)
    …and many other more recent studies,
    e.g., see Journal of Chinese Linguistics (2015), Vol. 43 (1B)
    for collection of articulatory/physiological studies

    View Slide

  13. UCHICAGO BEIJING: CREAK
    TALK OVERVIEW: QUANTITATIVE METHODS
    13
    ▸Quantitative methods for studying under-studied
    languages
    ▸Intro to quantitative methods for acoustic study of
    phonation (should be possible to apply to already-
    existing recordings)
    ▸Methodological points about: (1) challenges of
    working with highly collinear, high-dimensional data,
    and (2) the importance of analyzing time-course of
    measured parameters in addition to parameter means
    ▸ Exploratory cluster analysis to see what dimensions
    underlie clusters are identified in “creak” space

    View Slide

  14. CREAKY VOICE AND
    LINGUISTIC STRUCTURE:
    14
    ▸ Phonemic contrast (Mpi)
    ▸ Contrast enhancement (low falling tones)
    ▸ Prosodic phrasing (phrase-final creak)
    ▸ Use of creaky in tonal perception

    View Slide

  15. UCHICAGO BEIJING: CREAK 15
    CREAKY VOICE PHONEMIC CONTRAST EXAMPLE
    http://phonetics.ucla.edu/vowels/chapter12/mpi.html
    Creaky voice contrast across all
    pitch contour types

    View Slide

  16. UCHICAGO BEIJING: CREAK 16
    Mandarin: Kuang (2017)+refs within; Cantonese: Yu & Lam (2014) + refs within; Hmong: Esposito (2012), Garellek et al. (2013) +
    refs within; creaky falling/dipping tone typology, “Contour-Unspecified Low Tone”: Zhu (2015, §2), Zhu et al. (2012).
    Mandarin Cantonese
    White
    Hmong
    Level 55 55, 33, 22 55, 33, 22
    Rise 35 25, 23 24
    High fall 51 42, 52
    (breathy contrast)
    Low fall 21 21 21
    CREAK IN LOW FALLING TONES ACROSS LANGUAGES
    creaky
    Lowest falling tone often produced with creak…

    View Slide

  17. UCHICAGO BEIJING: CREAK 17
    CREAK IN ALL MANDARIN TONES, NOT JUST 21
    …and likelihood of creak
    increases with prosodic
    boundary strength…
    Kuang (2018), Fig. 2a

    View Slide

  18. UCHICAGO BEIJING: CREAK 18
    LISTENERS ARE SENSITIVE TO CREAK TYPES…
    Listeners can discriminate between /t/-glottalization and
    phrase-final creak in American English (little confusability
    in button vs. bun, whether phrase-final or not)
    Garellek (2015), Fig. 1
    /button/,
    non-phrase final
    /button/,
    phrase final

    View Slide

  19. UCHICAGO BEIJING: CREAK 19
    PITCH-DEPENDENT VOICE QUALITY CONTINUUM
    Adapted from Kuang (2017): Figure 4
    Falsetto
    Tense
    Modal
    Vocal fry
    Pitch scale
    Creak in production of
    all Mandarin tones, not
    just 21.
    Non-modal phonation
    can be driven by
    extreme pitch targets.
    At low end of pitch
    scale: vocal fry (or
    breathy) phonation.
    (Kuang 2017)
    (or breathy)
    High
    Low

    View Slide

  20. UCHICAGO BEIJING: CREAK 20
    Zhu (2012)
    “MULTI-REGISTER AND FOUR-LEVEL MODEL”
    6–3
    5-2
    4-1
    Pitch scale

    View Slide

  21. UCHICAGO BEIJING: CREAK 21
    CREAKY VOICE QUALITY ENHANCES LOW PITCH PERCEPT
    Zhu, Zhang & Yi (2012)
    5
    4
    3
    2
    1
    Pitch scale
    High
    Low
    Idea: Creaky voice
    quality enhances low
    pitch, i.e., percept of
    extra-low “0” on
    pitch scale

    …and see Kuang & Liberman
    (2016), The effect of vocal fry on
    pitch perception. ICASSP-2016.
    languagelog.ldc.upenn.edu/
    myl/KuangICASSP2016.pdf

    View Slide

  22. UCHICAGO BEIJING: CREAK 22
    IS CREAK USED IN PERCEPTION OF 21 TONES?
    ▸ Mandarin:
    ▸ Yes, (Yang 2015), bias for 21 identification in stimuli with f0 contours resynthesized with PSOLA
    from naturally produced tones in isolated syllables, also Belotel-Grenié and Grenié (1997)
    ▸ No (Gårding et al. 1986; Cao 2012, “creak” manually added abrupt f0 drop, “jitter” in f0 contour)
    ▸ Yes, but only for “extra-low” manipulation and not other kinds of creak properties: Huang (2019,
    2019a) with Praat KlattGrid synthesized stimuli
    ▸ Cantonese:
    ▸ Yes, creaky 21 tones identified more accurately than non-creaky 21 tones; creak biases tone
    identification between 21 and 22 towards 21 (Yu and Lam 2014, naturally produced stimuli)
    ▸ No (Zhang and Kirby in prep, Praat KlattGrid synthesized stimuli, period doubling parameter
    manipulation)
    ▸ Hmong:
    ▸ No, creak doesn’t bias tone identification between 21 and 22 towards 21, f0 contours
    resynthesized with PSOLA from naturally produced tones in isolated syllable (Garellek et al.
    2013)
    In sum…mixed results

    View Slide

  23. UCHICAGO BEIJING: CREAK 23
    WHY INCONSISTENT RESULTS?
    ▸ Major source of variation between experimental tasks:
    difference in design of experimental stimuli
    ▸ Naturally produced stimuli (from recordings)
    ▸ Resynthesized stimuli (Praat PSOLA resynthesis of f0 contour,
    manual editing of f0 contour)
    ▸ Synthesized stimuli from scratch (e.g., Praat KlattGrid synthesizer,
    different parameters manipulated in synthesis)?
    To begin to understand why: we need to
    better understand natural variation in
    production (and perception) of “creak”

    View Slide

  24. TYPES OF CREAK
    24

    View Slide

  25. UCHICAGO BEIJING: CREAK
    MANY DIFFERENT TERMS FOR “CREAK”!
    25

    View Slide

  26. UCHICAGO BEIJING: CREAK
    PROPOSED BREAKDOWN OF CREAK TYPES
    26
    Keating, Garellek and Kreiman (2015)

    View Slide

  27. UCHICAGO BEIJING: CREAK
    THREE ATOMIC PROPERTIES OF CREAK
    27
    Garellek (2019),
    simplification of
    Keating et al. (2015)
    Idea: presence of just one of these properties
    individually already sufficient to generate
    perception of creaky voice quality.
    ▸ Percept: Constricted voicing
    ▸ Acoustic correlate: spectral tilt
    ▸ Percept: Irregular pitch
    ▸ Acoustic correlate: noise
    ▸ Percept: Low pitch
    ▸ Acoustic correlate: fundamental frequency (f0)

    View Slide

  28. UCHICAGO BEIJING: CREAK
    LOW PITCH ALONE INDUCES CREAK PERCEPT
    28
    Time (s)
    0.1064 0.2067
    -0.9012
    0.9663
    0
    0.144962364
    Time (s)
    0.1064 0.2067
    -0.8999
    0.9681
    0
    0.144962364
    f0 = 100 Hz
    f0 = 60 Hz
    Synthesized
    with Praat
    KlattGrid,
    all parameters
    other than f0
    held constant

    View Slide

  29. UCHICAGO BEIJING: CREAK
    HYPOTHESIS: DISTINCT TYPES OF CREAK?
    29
    https://pages.ucsd.edu/~mgarellek/files/Garellek_Phonetics_of_Voice_Handbook_final.pdf
    Garellek (2019: Fig. 3),
    Keating et al. (2015)
    Mpi: tense voice
    (not f0-dependent)
    “21” tones: low pitch?

    View Slide

  30. UCHICAGO BEIJING: CREAK
    DISTINCT TYPES OF CREAK: CANTONESE 21
    30
    Time (s)
    0.8 0.9
    -0.4798
    0.3565
    0
    Narrow-band spectrograms Waveforms
    Time (s)
    1.09 1.19
    -0.1679
    0.2286
    0
    Time (s)
    0.7288 1.043
    0
    1000
    Frequency (Hz)
    0.728834954
    Time (s)
    1.055 1.276
    0
    1000
    Frequency (Hz)
    1.05537273 1.27621711
    Prototypical
    Multiply
    pulsed
    jiu33 lau lau jak̚3
    sub-harmonics

    View Slide

  31. UCHICAGO BEIJING: CREAK
    DOUBLY PULSED (PERIOD DOUBLED) CREAK: WAVEFORM
    31
    A period doubled waveform.
    Arrows mark the pattern of alternating cycles.
    Two periods: doubled period
    results in sub-harmonics
    Acoustic correlate: high subharmonics-to-
    harmonics ratio (SHR), a special kind of noise

    View Slide

  32. UCHICAGO BEIJING: CREAK
    HYPOTHESIS: DISTINCT TYPES OF CREAK?
    32
    https://pages.ucsd.edu/~mgarellek/files/Garellek_Phonetics_of_Voice_Handbook_final.pdf
    Idea: Creak types can
    be understood in terms
    of combinations of the
    three properties.
    Garellek (2019)
    Example: Vocal fry
    ☑Constricted voicing
    ☑low pitch
    ☒NO* irregular pitch
    (*simplification: see Keating et al. (2015 §§3.1,4.2,4.4)

    View Slide

  33. UCHICAGO BEIJING: CREAK
    ACOUSTIC MEASURES FOR CREAK PROPERTIES
    33
    Garellek (2019), Keating et al. (2015)
    ▸ Constricted voicing (spectral tilt)
    ▸ Lower H1-H2 㱺 lower spectral tilt 㱺 More constricted
    ▸ Irregular pitch (noise)
    ▸ Lower harmonics-to-noise-ratio (HNR) 㱺 noisier
    㱺 more irregularity in pitch
    ▸ Low pitch (f0)
    ▸ Lower f0 㱺 lower pitch
    Example: Vocal fry
    ☑Constricted voicing: lower H1-H2
    ☒NO irregular pitch: higher HNR
    ☑low pitch: lower f0

    View Slide

  34. UCHICAGO BEIJING: CREAK 34
    VIDEOKYMOGRAPHIC IMAGES OF PHONATION
    https://www.researchgate.net/figure/Videokymographic-images-of-the-vibration-of-the-vocal-folds-during-phonation-in-the-three_fig3_241682145
    Vocal cords are mostly constricted during vocal fry.
    Spectral tilt (H1-H2) is acoustic
    correlate of proportion of closure
    during phonation cycle.
    (review: Kreiman et al. 2014, §4)

    View Slide

  35. LINGUIST 414, SPRING 2019. CLASS 4.1
    SPECTRAL TILT: H1-H2 IN THE SPECTRUM
    35
    1st harmonic: frequency f0, amplitude H1
    2nd harmonic: frequency 2*f0, amplitude H2
    H1
    H2

    View Slide

  36. LINGUIST 414, SPRING 2019. CLASS 4.1
    LOWER H1-H2: MORE CONSTRICTED VOICING
    36
    H1
    H2
    Spectrum of modal [a] Spectrum of creaky [a]
    Adapted from Kong (1996, 42) on Hani language
    H1
    H2
    Higher H1-H2 Lower H1-H2

    View Slide

  37. UCHICAGO BEIJING: CREAK
    HARMONICS TO NOISE RATIO (HNR) IN SPECTRUM
    37
    Low noise: high HNR
    High noise: low HNR
    https://slideplayer.com/slide/13152153/79/images/13/Harmonics-to-noise+Ratio.jpg
    Cepstral peak prominence (CPP):
    related measure with same
    correlations: low noise, high CPP

    View Slide

  38. ACOUSTIC ANALYSIS:
    “21” ACROSS LANGUAGES
    AND EXPLORING CREAK TYPES
    38

    View Slide

  39. UCHICAGO BEIJING: CREAK
    DATA OVERVIEW
    39
    ▸ Tones recorded sentence-medially in carrier phrase, ranging
    over all possible bitone combinations, e.g., ma ma, ma má, etc.
    ▸ In connected speech, not in isolation
    ▸ 12 speakers per language, 8 currently analyzed for Beijing
    Mandarin and 3 for Taiwan Mandarin
    ▸ Cantonese and Taiwan Mandarin recorded in Los Angeles,
    California; Hmong recorded in Fresno, California; Beijing
    Mandarin recorded in Beijing
    ▸ (Warning: of course results about across-language
    comparisons are limited by small size of sample of
    participants)

    View Slide

  40. UCHICAGO BEIJING: CREAK
    RECALL - TALK OVERVIEW: QUANTITATIVE METHODS
    40
    ▸Quantitative methods for studying under-studied
    languages
    ▸Intro to quantitative methods for acoustic study of
    phonation (should be possible to apply to already-
    existing recordings)
    ▸Methodological points about: (1) challenges of
    working with collinearity in high-dimensional data,
    and (2) the importance of analyzing time-course of
    measured parameters in addition to parameter means
    ▸ Exploratory cluster analysis to see what clusters are
    identified in “creak” space

    View Slide

  41. UCHICAGO BEIJING: CREAK 41
    SPECTRAL MEASURES OF 21 VS. {22, 51}
    Mandarin Cantonese
    White
    Hmong
    Level 55 55, 33, 22 55, 33, 22
    Rise 35 25, 23 24
    High fall 51 42, 52
    (breathy contrast)
    Low fall 21 21 21

    View Slide

  42. UCHICAGO BEIJING: CREAK
    AUTOMATING MEASUREMENT OF SPECTRAL PROPERTIES
    42
    http://www.phonetics.ucla.edu/voicesauce/

    View Slide

  43. UCHICAGO BEIJING: CREAK
    VOICESAUCE REQUIRES MATLAB AND/OR WINDOWS
    43
    http://www.phonetics.ucla.edu/voicesauce/

    View Slide

  44. UCHICAGO BEIJING: CREAK
    PARAMETERS MEASURED BY VOICESAUCE
    44
    Huge number of parameters
    measured that are related
    (high-dimensionality,
    collinearity).
    And that’s not even taking into
    account that each parameter
    takes place over a time course.
    Dimensionality reduction
    helpful. (Faytak tutorial Sunday)

    View Slide

  45. UCHICAGO BEIJING: CREAK
    ANALYSIS OVERVIEW
    45
    ▸ What parameters best distinguish between 21 and {22,51}?
    ▸ Functional data analysis (FDA) with principal components
    analysis (PCA) to capture time course shape in low-
    dimensional space (see appendix)
    ▸ Random forests to compute which parameters best
    discriminate tone categories, while dealing with collinearity in
    large number of parameters (see appendix)
    ▸ How do “21” utterances cluster in “creak” space (HNR, H1-H2,
    f0)?
    ▸ Exploratory hierarchical cluster analyses to discover potential
    underlying clusters of creak types in data

    View Slide

  46. UCHICAGO BEIJING: CREAK
    PRINCIPAL COMPONENTS OF F0 CURVES
    46
    Functional
    principle
    components
    decomposes
    each f0
    contour into a
    weighted sum
    of atomic
    curve shape
    components.

    View Slide

  47. UCHICAGO BEIJING: CREAK
    F0 PRINCIPAL COMPONENTS 1 AND 2
    47
    Principal component 1
    mostly shifts curve up/down
    Principal component 2 tilts
    the curve like a see-saw
    http://www.playground-china.com/8165ISeesaws2543.html
    https://i.redd.it/8yc3gk1g2ev01.jpg

    View Slide

  48. UCHICAGO BEIJING: CREAK
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    2121
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    2121
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    51
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    -100
    -50
    0
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    100
    -200 0 200 400
    Principle component 1
    Principle component 2
    51
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    -50
    0
    50
    100
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    200
    -100 0 100 200
    Principle component 1
    Principle component 2
    21 VS. 51 IN BEIJING VS. TAIWAN MANDARIN: F0
    48
    Taiwan Beijing
    Principal component 2
    best discriminates 21/51
    Principal component 1
    best discriminates 21/51
    You miss this distinction if you look only at mean f0.

    View Slide

  49. UCHICAGO BEIJING: CREAK
    21 VS. 22 IN HMONG VS. CANTONESE: H1
    49
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    -60 -40 -20 0 20
    Principle component 1
    Principle component 2
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    -10
    0
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    -50 -25 0 25
    Principle component 1
    Principle component 2
    White Hmong Cantonese
    Principal component 1
    best discriminates 21/22
    Principal component 2
    best discriminates 21/22
    You miss this distinction if you look only at mean H1.

    View Slide

  50. UCHICAGO BEIJING: CREAK
    CLUSTER ANALYSIS OVERVIEW
    50
    ▸ How do the “creak space” measures determine dimensions
    underlying discovered clusters?
    ▸ Principal components analysis reveal independent
    dimensions: are the three types of creak space measures
    independent?
    ▸ Noise: Harmonics to noise ratio (HNR), Cepstral peak
    prominence (CPP), Subharmonics-to-harmonics ratio
    (SHR) for multiply pulsed/doubly pulsed creak
    ▸ Fundamental frequency (f0)
    ▸ Spectral tilt (H1-H2)
    ▸ How do “21” utterances cluster in creak space (HNR, H1-H2, f0)?

    View Slide

  51. UCHICAGO BEIJING: CREAK
    3D CREAK SPACE: (HNR, H1-H2, F0)
    51
    High noise: low
    harmonics-to-noise
    ratio (HNR)
    Low f0
    Low H1-H2
    347
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    57
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    130
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    116
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    53
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    31
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    13
    11
    325
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    0 10 20 30 40 50
    Cluster Dendrogram
    hclust (*, "ward.D2")
    d
    Height

    View Slide

  52. UCHICAGO BEIJING: CREAK
    WHICH MEASURES DETERMINE PCA DIMENSIONS?
    52
    Hmong
    0
    0.1
    0.2
    0.29
    0.39
    0.49
    0.59
    0.69
    0.78
    0.88
    0.98
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Beijing
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Taiwan
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Cantonese
    The darker the circle, the more that measure
    dominates the PCA dimension

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  53. UCHICAGO BEIJING: CREAK
    WHICH MEASURES DETERMINE PCA DIMENSIONS?
    53
    Hmong
    0
    0.1
    0.2
    0.29
    0.39
    0.49
    0.59
    0.69
    0.78
    0.88
    0.98
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Beijing
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Taiwan
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Cantonese
    Noise measures make up one PCA dimension.

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  54. UCHICAGO BEIJING: CREAK
    WHICH MEASURES DETERMINE PCA DIMENSIONS?
    54
    Hmong
    0
    0.1
    0.2
    0.29
    0.39
    0.49
    0.59
    0.69
    0.78
    0.88
    0.98
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Beijing
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Taiwan
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Cantonese
    SHR (subharmonics, correlate of doubly pulsed
    creak) mostly independent of other noise measures.

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  55. UCHICAGO BEIJING: CREAK
    WHICH MEASURES DETERMINE PCA DIMENSIONS?
    55
    Hmong
    0
    0.1
    0.2
    0.29
    0.39
    0.49
    0.59
    0.69
    0.78
    0.88
    0.98
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Beijing
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Taiwan
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Cantonese
    F0 and H1-H2 often travel together in a dimension.

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  56. UCHICAGO BEIJING: CREAK
    WHICH MEASURES DETERMINE PCA DIMENSIONS?
    56
    Hmong
    0
    0.1
    0.2
    0.29
    0.39
    0.49
    0.59
    0.69
    0.78
    0.88
    0.98
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Beijing
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    Dim.5
    Dim.6
    Dim.7
    Dim.8
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Taiwan
    Dim.1
    Dim.2
    Dim.3
    Dim.4
    H1H2c_mean
    strF0_mean
    SHR_mean
    HNR05_mean
    HNR15_mean
    HNR25_mean
    HNR35_mean
    CPP_mean
    Cantonese
    “Creak dimensions” least independent in Beijing
    Mandarin and Cantonese.

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  57. UCHICAGO BEIJING: CREAK
    HMONG 21 CLUSTERS IN 3D CREAK SPACE
    57
    Low f0, H1-H2, HNR:
    Not irregular pitch?
    High f0, high HNR:
    prototypical/unconstricted
    High H1-H2,
    Lower f0, HNR:
    unconstricted?
    High f0, H1-H2, HNR: Modal

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  58. UCHICAGO BEIJING: CREAK
    CANTONESE 21 CLUSTERS IN 3D CREAK SPACE
    58

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  59. UCHICAGO BEIJING: CREAK
    2D CREAK SPACE: CONSTRICTION BY F0
    59
    Hmong Cantonese
    -4
    -2
    0
    2
    -2 0 2 4
    strF0_mean
    H1H2c_mean
    cluster
    1
    2
    3
    4
    Cluster plot
    -3
    -2
    -1
    0
    1
    2
    -2 0 2
    strF0_mean
    H1H2c_mean
    cluster
    1
    2
    3
    4
    Cluster plot

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  60. UCHICAGO BEIJING: CREAK
    2D CREAK SPACE: NOISE BY F0
    60
    Hmong Cantonese
    -2
    -1
    0
    1
    2
    -2 0 2 4
    strF0_mean
    HNR15_mean
    cluster
    1
    2
    3
    4
    Cluster plot
    -2
    -1
    0
    1
    2
    -2 0 2
    strF0_mean
    HNR15_mean
    cluster
    1
    2
    3
    4
    Cluster plot

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  61. UCHICAGO BEIJING: CREAK
    2D CREAK SPACE: NOISE BY CONSTRICTION
    61
    Hmong Cantonese
    -2
    -1
    0
    1
    2
    -4 -2 0 2
    H1H2c_mean
    HNR15_mean
    cluste
    1
    2
    3
    4
    Cluster plot
    -2
    -1
    0
    1
    2
    -3 -2 -1 0 1 2
    H1H2c_mean
    HNR15_mean
    cluster
    1
    2
    3
    4
    Cluster plot

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  62. UCHICAGO BEIJING: CREAK
    CONCLUSION
    62
    ▸ Idea: recognizing types of creaky voice can help us understand how variation
    in creaky voice is related to linguistic structure:
    ▸ Vocal Fry ⊂ Creaky Voice
    ▸ 3D “creak space” of noise (harmonics-to-noise-ratios, etc.), fundamental
    frequency (f0), and constricted voice (H1-H2) does emerge in cluster
    discovery
    ▸ Methodological points
    ▸ Intro to quantitative methods for acoustic study of phonation:
    dimensionality reduction can help us understand important features of time-
    course of highly collinear high-dimensional voice quality measures for tone
    classification
    ▸ Further challenges: Is f0 even well-defined? Spectral measures largely
    based on f0! Non-visual inspection/automatic creak detection (see appendix)

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  63. UCHICAGO BEIJING: CREAK
    REFERENCES
    63
    ▸ Cao, R. (2012). Perception of Mandarin Chinese Tone 2/Tone 3 and the role of creaky voice. Doctoral dissertation. University of Florida.
    ▸ Gårding, E., Kratochvil, P., Svantesson, J. O., and Zhang, J. (1986). Tone 4 and Tone 3 discrimination in modern standard Chinese. Language
    and Speech, 29:281- 293.
    ▸ Garellek, M. (2019). The phonetics of voice. In W. F. Katz & P. F. Assmann eds., The Routledge Handbook of Phonetics: 75-106. http://
    idiom.ucsd.edu/~mgarellek/files/Garellek_Phonetics_of_Voice_Handbook_final.pdf
    ▸ Garellek, M., Keating, P., Esposito, C. M., Kreiman, J. 2013. Voice quality and tone identification in White Hmong. JASA 133, 1078-1089.
    ▸ Huang, Yaqian. (2019). Low f0 as a creak attribute in Mandarin tone perception. LSA 2019 https://drive.google.com/open?
    id=10iURFJ_fu9rPPYgcdRVYOHVjtA-ac1vZ
    ▸ Kreiman and Sidtis (2011). Foundations of Voice Studies. Wiley Blackwell. https://onlinelibrary.wiley.com/doi/book/
    10.1002/9781444395068
    ▸ Gerratt, B. R., and Kreiman, J. (2001). “Toward a taxonomy of nonmodal phonation,” J. Phonetics 29, 365–381.
    ▸ Kong, Jiangping. (1996). Hani yu fa sheng lei xing sheng xue yanjiu ji yin zhi gai nian de tao lun [Study on phonation types and timbre of
    Hani language]. Min zu yu wen [Minority Languages of China] 1, 40-46.
    ▸ Kong, Jiangping and Gaowu Wang. (2013). Contemporary voice research: a China perspective.
    ▸ Kreiman, J., Gerratt, B. R., Garellek, M., Samlan, R., and Zhang, Z. (2014). Toward a unified theory of voice production and perception.
    Loquens, 1(1), e009. doi: http://dx.doi.org/10.3989/loquens.2014.009
    ▸ Kuang, Jianjing. (2017) Covariation between voice quality and pitch: Revisiting the case of Mandarin creaky voice. JASA https://doi.org/
    10.1121/1.5003649
    ▸ Kuang, Jianjing. (2018). The influence of tonal categories and prosodic boundaries on the creakiness in Mandarin. JASA 143, EL509 (2018);
    doi: 10.1121/1.5043094
    ▸ Yang, Ruoxiao. (2015). The role of phonation cues in Mandarin tonal perception. J. Chin. Ling. 43(2): 453-472, https://www.jstor.org/stable/
    24774954
    ▸ Yu, K. M., and Lam, H. W. (2014). The role of creaky voice in Cantonese tonal perception. JASA, 136(3), 1320-1333. 38
    ▸ Zhang, Yubin and James Kirby (2018). Variation in Cantonese Tone 4 and Tone 6 perception. The 30th North American Conference on
    Chinese Linguistics (NACCL-30)
    ▸ Zhu, Xiaonong. (2012). Multiregisters and Four Levels: A New Tonal Model. https://www.jstor.org/stable/23754196
    ▸ Zhu, Xianong. (2015). Types of falling tones. J. Chin. Ling. 43(2): 605-637. https://www.jstor.org/stable/24774975
    ▸ Zhu Xiaonong 朱晓农,Zhang Ting 章婷 and Yi Li 衣莉. (2012). 凹调的种类 (A classification of dipping tones). Zhongguo Yuwen 中国语⽂文 5:
    420-436.

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  64. APPENDIX
    64

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  65. UCHICAGO BEIJING: CREAK
    IPA VOICE QUALITY SYMBOLS EXTENSION
    65
    https://doi.org/10.1017/S0025100317000159

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  66. UCHICAGO BEIJING: CREAK
    FUNCTIONAL PRINCIPAL COMPONENTS RESOURCES
    66
    ▸ http://lands.let.ru.nl/FDA/ (Michele Gubian)
    ▸ Gubian workshop materials: https://github.com/
    uasolo/FPCA-phonetics-workshop
    ▸ http://www.psych.mcgill.ca/misc/fda/ (older, but can still
    be useful)
    ▸ faculty.bscb.cornell.edu/~hooker/
    ShortCourseHandout.pdf, http://faculty.bscb.cornell.edu/
    ~hooker/FDAWorkshop/ (Giles Hooker)

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  67. UCHICAGO BEIJING: CREAK
    WORKING WITH COLLINEAR DATA
    67
    ▸ Strategies for addressing collinearity in multivariate
    linguistic data (Tomaschek, Peter Hendrix, R. Haraald
    Baayen, J. Phonetics, 2018)
    ▸ https://www.sciencedirect.com/science/article/pii/
    S0095447017302292
    ▸ Beware Default Random Forest Importances
    ▸ https://explained.ai/rf-importance/index.html

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  68. UCHICAGO BEIJING: CREAK
    AUTOMATIC CREAK DETECTION
    68
    ▸ Drugman, T., Kane, J., Gobl, C. (requires Matlab)
    ▸ https://github.com/covarep/covarep/tree/master/
    glottalsource/creaky_voice_detection
    ▸ See also Kuang (2018)

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  69. EXTRA SLIDES
    69

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  70. UCHICAGO BEIJING: CREAK 70
    PITCH-DEPENDENT VOICE QUALITY CONTINUUM
    Zhu (2015)

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  71. UCHICAGO BEIJING: CREAK
    EXAMPLE OF DAMPING IN WAVEFORM
    71
    https://www.toppr.com/guides/physics/oscillations/damped-simple-harmonic-motion/

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  72. UCHICAGO BEIJING: CREAK 72
    MULTI-REGISTER AND FOUR-LEVEL MODEL
    Zhu (2012)

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