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?

2

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

3

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

4

phonological neighborhood density

High-density words are hard for perception but easy for production (Dell & Gordon, 2003)

Introduction | Methodology | Linear mixed-effects model | Discussion

5

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

6

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

7

• 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

8

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

9

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

10

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

11

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

12

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

13

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

14

linear mixed-effects model

• Fixed effects– All predictors

• Neighborhood measures• Control variables

• Random effects– Speaker– Word

Introduction | Methodology | Linear mixed-effects model | Discussion

15

modeling results

• Neighborhood density– A significant negative effect– More neighbors shorter duration– Facilitation

• Neighbor frequency – Insignificant

Introduction | Methodology | Linear mixed-effects model | Discussion

16

partial effect of neighborhood density Introduction | Methodology | Linear mixed-effects model | Discussion

Effect confirmed by model evaluation.

17

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

18

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…

19

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

20

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

21

The end…

Introduction | Methodology | Linear mixed-effects model | Discussion

22

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

23

Thanks to…

• Prof. Susanne Gahl and Prof. Keith Johnson for helpful discussion

• Anonymous subjects in Buckeye• Buckeye corpus developers

24

Perception & Production

fat

fad

fight kite

capadd

catcoat

ProductionPerception

Dell & Gordon (2003)

25

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

26

Individual differencesIntroduction | Methodology | Linear mixed-effects model | Discussion

27

Having one more neighbor decreases duration by 0.4%

Distribution of neighborhood density and neighbor frequency

Introduction | Methodology | Linear mixed-effects model | Discussion

28