yao yao @ lsa 2010-1-7 separating speaker- and listener- oriented forces in speech – evidence from...
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Yao Yao @ LSA2010-1-7
Separating speaker- and listener-oriented forces in speech
– Evidence from phonological neighborhooddensity
phonetic variationIntroduction | Methodology | Linear mixed-effects model | Discussion
• Widely exists in spontaneous speech– Duration– Segmental realization– Pitch
• Why?
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explaining variation
Listener-oriented• Response to different models
of listener’s needs• Result of ease or difficulty of
comprehension (modeled by the speaker)
• Examples– Foreigner- and child-directed
speech– Speech under noise– Shortening and reduction in
• High-frequency or high-predictability forms
Talker-oriented• Result of ease or difficulty
of production• Examples
– Shortening and reduction in • High-frequency or high-
predictability forms• “articulatory routinization”
(Bybee, 2001)
Many word properties have the same predictions for comprehension and production…
Introduction | Methodology | Linear mixed-effects model | Discussion
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general research question– Is it possible to tease apart talker- and listener-
oriented forces in variation at the word level?
Any word property with different predictions for comprehension and production? Yes!
Introduction | Methodology | Linear mixed-effects model | Discussion
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phonological neighborhood density
High-density words are hard for perception but easy for production (Dell & Gordon, 2003)
Introduction | Methodology | Linear mixed-effects model | Discussion
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phonological neighborhood• Concept
– Similar-sounding words are connected to each other and form phonological neighborhoods
– Neighborhood density: number of phonological neighbors each word has
• One-phoneme difference rule (Luce & Pisoni 1998, etc)
Introduction | Methodology | Linear mixed-effects model | Discussion
Additional factors: neighborhood freq. Additional factors: neighborhood freq.
fat
fad
fight kite
capadd
catcoat
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phonological neighbors and word perception
• Inhibition– Similar-sounding primes inhibit auditory word
recognition (Goldinger & Pisoni 1989)
– Slower (and less accurate) responses for words from dense neighborhoods in perceptual tasks (Luce & Pisoni 1998)
• Perceptual identification, lexical decision and word naming tasks
Introduction | Methodology | Linear mixed-effects model | Discussion
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• Facilitation – Words from dense neighborhoods induce fewer
speech errors and have shorter latency times in picture naming tasks (Vitevitch 2002)
Introduction | Methodology | Linear mixed-effects model | Discussion
phonological neighbors and word production
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phonological neighbors and phonetic variation
• Phonological neighbors– Both compete with and bring more activation to
the target word– Either impede or facilitate the processing of the
target word
• How does neighborhood density tease apart the two accounts of variation?
Introduction | Methodology | Linear mixed-effects model | Discussion
perceptionperception productionproduction
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predictions
• Talker-oriented– High-density words are easy to produce
shortening and reduction
• Listener-oriented – High-density words are hard to perceive
lengthening and vowel dispersion• High-density words have more expanded vowel space
(Wright 1997, Munson & Solomon 2004) and more nasalized vowels (Scarborough 2004)
Introduction | Methodology | Linear mixed-effects model | Discussion
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keywords of current study
• Spontaneous speech• Aspects of production
– Word duration– Vowel production
• High-density words are shorter talker-oriented
Introduction | Methodology | Linear mixed-effects model | Discussion
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data
• Buckeye corpus (Pitt et al 2007)• 40 speakers, ~300,000 words• Target words
– CVC– Monomorphemic– Content words
• 414 word types / 13,858 tokens
Introduction | Methodology | Linear mixed-effects model | Discussion
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neighborhood measures
• Two separate variables (from Hoosier Mental Lexicon; Nusbaum et al, 1984)
– Neighborhood density (i.e. # of neighbors)• Using the 1-phoneme difference rule
– Average neighbor frequency
Introduction | Methodology | Linear mixed-effects model | Discussion
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coding variables• Outcome variable
– Word token duration• Control variables
– Baseline duration– Speaker characteristics
• sex, age – Other lexical properties
• word freq, length (in letters), familiarity, imageability, POS, phonotactic probability
– Contextual factors• pre/fw predictability, pre/fw speech rate, disfluency, pre
mentions
Introduction | Methodology | Linear mixed-effects model | Discussion
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linear mixed-effects model
• Fixed effects– All predictors
• Neighborhood measures• Control variables
• Random effects– Speaker– Word
Introduction | Methodology | Linear mixed-effects model | Discussion
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modeling results
• Neighborhood density– A significant negative effect– More neighbors shorter duration– Facilitation
• Neighbor frequency – Insignificant
Introduction | Methodology | Linear mixed-effects model | Discussion
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partial effect of neighborhood density Introduction | Methodology | Linear mixed-effects model | Discussion
Effect confirmed by model evaluation.
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confounding factor?• Phonotactic probability
– The frequency with which a phonological segment, […] and a sequence of phonological segments, […] occur in a given position in a word (Jusczyk et al, 1994)
– Correlated with neighborhood density (r = 0.46)– Phonotactic probability is never significant in the model,
with or without neighborhood measures
• The facilitative effect is at the lexical level, not the sublexical level
Introduction | Methodology | Linear mixed-effects model | Discussion
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implications
• Evidence for talker-oriented account– Talker-oriented: High-density words are
easy to produce shortening and reduction– Listener-oriented: High density words are
hard to perceive lengthening and vowel dispersionFast lexical
access?Ease of articulation?
Not really…Probably…
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Introduction | Methodology | Linear mixed-effects model | Discussion
Synchrony between planning and articulation (Bell et al, 2009)
looking back…• Conflict with previous experimental results?
– Wright (1997) and Munson & Solomon (2004): Vowel dispersion in high-density words
– Shorter but more expanded vowels?– Differences in the type of speech?– Maybe it’s not density, but neighbor frequency…
• Preliminary results in the current dataset: NO effect of density, but words with high-frequency neighbors have more expanded vowel space
• Previous results can also be explained by neighbor frequency
Introduction | Methodology | Linear mixed-effects model | Discussion
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conclusion
• Facilitative effect of neighorhood density on word duration
• Unambiguous evidence for the talker-oriented account of phonetic variation
• Ongoing work: effect of phonological neighborhoods on vowel production
Introduction | Methodology | Linear mixed-effects model | Discussion
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The end…
Introduction | Methodology | Linear mixed-effects model | Discussion
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selected references• Dell & Gordon(2003). Neighbors in the lexicon: Friends or foes? In N.O. Schiller and
A.S. Meyer (eds.), Phonetics and phonology in language comprehension and production: Differences and similarities. New York: Mouton.
• Luce & Pisoni (1998) Recognizing spoken words: the Neighborhood Activation Model. Ear & Hearing, 19, 1-36.
• Munson & Solomon (2004) The effect of phonological neighborhood on vowel articulation. JSLHR, 47, 1048-1058.
• Pitt et al (2007Buckeye Corpus of Conversational Speech (2nd release) [www.buckeyecorpus.osu.edu] Columbus, OH: Department of Psychology, Ohio State University (Distributor).
• Scarborough (2004). Lexical confusability and degree of coarticulation. Proceedings of the 29th Annual Meeting of the
• Berkeley Linguistics Society.• Vitevitch (2002) The influence of phonological similarity neighborhoods on speech
production. J. of Experimental Psychology: Learning, Memory and Cognition, 28, 735-747.
• Wright (1997) Lexical competition and reduction in speech: A preliminary report. . Research on Spoken Language Processing Progress Report. 21, 471-485. Indiana University
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Thanks to…
• Prof. Susanne Gahl and Prof. Keith Johnson for helpful discussion
• Anonymous subjects in Buckeye• Buckeye corpus developers
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Perception & Production
fat
fad
fight kite
capadd
catcoat
ProductionPerception
Dell & Gordon (2003)
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Introduction | Methodology | Linear mixed-effects model | Discussion
model evaluation
• Confirms the robustness of the results– Testing t-values– Model comparison– Cross-validation
Introduction | Methodology | Linear mixed-effects model | Discussion
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Individual differencesIntroduction | Methodology | Linear mixed-effects model | Discussion
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Having one more neighbor decreases duration by 0.4%
Distribution of neighborhood density and neighbor frequency
Introduction | Methodology | Linear mixed-effects model | Discussion
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