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Voiced Voiceless
Aver
age
Vow
el L
engt
h (m
s)
Initial Stop Consonant
Vowel LengthHigh Engagement TaskLow Engagement Task
0100200300400500600700800900
1000
Voiced Voiceless
Aver
age
F1 (H
z)
Initial Stop Consonant
F1 OnsetHigh Engagement Task
Low Engagement Task
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Voiced Voiceless
Aver
age
f0 (H
z)
Initial Stop Consonant
F0 OnsetHigh Engagement …Low Engagement …
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VoicelessVoiced
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Normalized Trial Number (Proportion of total trials)
Voic
e on
set t
ime
(ms)
Stimulus Voicing: ● Voiceless Voiced
Initial VOT: ● ●Long Short
Voice Onset Time
β = 3.55, SE = 1.86,χ2(1) = 3.37, p = 0.073
β = 1.62, SE = 0.85,χ2(1) = 3.31, p = 0.073
High Engagement Low Engagement
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VoicelessVoiced
0.25 0.5 0.75 1 0.25 0.5 0.75 1
Normalized Trial Number (Proportion of total trials)
Vow
el le
ngth
(ms)
Stimulus Voicing: ● Voiceless Voiced
Initial VL: ● ●Long Short
Vowel Length
β = -8.78, SE = 2.72,χ2(1) = 7.82, p = .0053
β = -6 .17, SE = 3.18,χ2(1) = 3.51, p = .063
β = -3 .72, SE = 1.76,χ2(1) = 4.43, p = .042
High Engagement Low Engagement
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VoicelessVoiced
0.25 0.5 0.75 1 0.25 0.5 0.75 1
Normalized Trial Number (Proportion of total trials)
f0 o
nset
(Hz)
Stimulus Voicing: ● Voiceless Voiced
Initial f0 onset: ● ●High Low
Phonetic convergence in an immersive game-based task
INTRODUCTION
METHOD
RESULTS DISCUSSION
ACKNOWLEDGEMENTS & REFERENCES
Tifani Biro ([email protected])
Department of Speech-Language-Hearing: Sciences and Disorders
University of Kansas
Joe Toscano ([email protected])
Department of PsychologyVillanova University
Navin Viswanathan([email protected])
Department of Speech-Language-Hearing: Sciences and Disorders
University of Kansas
Statistical AnalysesThree linear mixed effects models were used to examine:
The effect of trial number, task engagement, initial VOT, and their interactions on the produced VOTs for voiced or voiceless tokens separately. A trial number x task engagement x initial VOT interaction would indicate convergence
The effect of initial VOT, trial number, and their interactions on VOTs of voiced or voiceless tokens separately in each individual task. A initial VOT x trial number interaction would indicate convergenceThe effect of trial number on the VOTs of voiced or voiceless tokens for each of the participants in each task
Similar models examined these effects on VL, F1, and f0
Summary of ResultsElicited mean VOT were longer than what had been previously reported (voiced VOT: 25 ms; voiceless VOT: 101 ms; Lisker & Abramson, 1964). Lexical factors (Baese-Berk & Goldrick, 2009) and task difficulty (Schertz, 2013) could contribute to these differencesHowever, participants’ productions were not significantly different between the low and high engagement tasks
Convergence was observed along certain acoustic dimensions (VOT, VL, F1 onset), but not others (f0). Interestingly, the extent of convergence was affected by the level of task engagement. Overall, subjects in the high engagement task converged more, whereas subjects in the low engagement task were less likely to converge. This finding was especially the case for VL and F1 onset
These preliminary results suggest that engaging, naturalistic tasks may yield results that more accurately reflect real-world phonetic variation than traditional laboratory experiments
Future DirectionsFuture studies will use these communicative tasks to look at convergence among speakers of different native languages
Participants completed either a high- or low-engagement task30 word-initial voicing minimal pairs provided key information that interlocutors had to provide to each otherPhonetic convergence was tracked for 4 acoustic dimensions (VOT, VL, F1, f0) over the course of the one hour experiment
AcknowledgementsThank you to our coders: Anne Marie Crinnion, Sarah Welsh, Jacklyn Coelho, Nicole Johnson, John Michael Kay, Rakshana Selvarajan, and Christopher Burley; thanks to David Saltzman and Michael Phelan for help programming the Minecraft puzzles, and thanks to Emma Folk for assistance with running subjects. TMB was supported by a Villanova Graduate Student Fellowship.
ReferencesBabel, M. (2012). Evidence for phonetic and social selectivity in spontaneous phonetic
imitation. Journal of Phonetics, 40(1), 177-189.Baese-Berk, M., & Goldrick, M. (2009). Mechanisms of interaction in speech production.
Language and cognitive processes, 24(4), 527-554.Buxó-Lugo, A., Toscano, J. C., & Watson, D. G. (2016). Effects of participant
engagement on prosodic prominence. Discourse Processes, (just-accepted).Lisker, L., & Abramson, A. S. (1964). A cross-language study of voicing in initial stops:
Acoustical measurements. Word, 20, 384-422.Olmstead, A. J., Viswanathan, N., Aivar, M. P., & Manuel, S. (2013). Comparison of
native and non-native phone imitation by English and Spanish speakers. Frontiers in psychology, 4.
Pardo, J. S. (2010). Expressing oneself in conversational interaction. Expressing Oneself/Expressing One's self: Communication, Cognition, Language, and Identity, 183-196.
Schertz, J. (2013). Exaggeration of featural contrasts in clarifications of misheard speech in English. Journal of Phonetics, 41(3), 249-263.
Mapping Acoustic Signals to Phonetic Categories
Stimuli (subset)
Phonetic category membership is indicated by multiple dimensions in the acoustic signal. However, acoustic dimensions are variable and context-dependent, which can lead to ambiguities between two speech sounds that only differ in one acoustic attribute. One phenomenon that may affect this variability is phonetic convergence (the observation that speech patterns of interlocutors become more similar during a conversation)
Procedure
1. Production Measures
The extent talkers can converge on productions outside of their native language range may be limited (Olmstead et al., 2013). What dimensions talkers converge on also varies across studies (Babel, 2012; Pardo, 2010). However, since convergence is a phenomenon occurring conversationally within the natural world, typical laboratory tasks may fail at fully eliciting it
2. Convergence Measures
High Engagement Low Engagement
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VoicelessVoiced
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Normalized Trial Number (Proportion of total trials)
F1 o
nset
(Hz)
Stimulus Voicing: ● Voiceless Voiced
Initial F1 onset: ● ●High Low
β = -25.56, SE = 12.63,χ2(1) = 3.99, p = .05
β = 14.18, SE = 6.01,χ2(1) = 4.48, p = .03
Factors such as engagement have been found to have an effect on language production (Buxó-Lugo et al., 2016)How might task engagement affect speech production and phonetic convergence?
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100120140160
b d g p t k
Aver
age
VOT
(ms)
Stop Consonant
VOT DistributionsLisker & Abramson (1964)
High Engagement Task
Low Engagement Task
Natural Setting• Conversational partner• Less control• Highly engaging
• Lack of interlocutor•More control• Typically boring
Laboratory Setting
Voice Onset Time (VOT)
Release Burst
f1 onset
f0 onset
Vowel Length
/b/
Time
Freq
Amp
Maze Participant 1 Participant 2
1 Listener Talker
2 Talker Listener
3 Listener Talker
4 Talker Listener
5 Listener Talker
6 Talker Listener
Voiced Voiceless
Velar got cot
goat coatghost coast
Alveolar dent tent
dip tipdart tart
Bilabial bat pat
bear pearbark park
gap fig
goat coat muck
luck sigh
high rig cap
say rare done pat yard
doe nun ten bet sad
pay heart frown glass try
goat muck fig high cap
Start
End
VOT
Conversation
/b/1 /b/2 /p/1 /p/2
VOT
/b/1 /b/2 /p/1 /p/2
F1 Onset Fundamental Frequency Onset
1
2
3
3
3
β = -8.67, SE = 3.97, χ2(1) = 4.73, p = .03
1β = -18.77, SE = 7.51,χ2(1) = 6.20, p = .01
2
Low Engagement Listener High Engagement Listener Low Engagement Talker High Engagement Talker
