Yao Yao @ LSA 2010-1-7

1. Separating speaker- and listener-oriented forces in speech–Evidence from phonological neighborhooddensity Yao Yao @ LSA 2010-1-7

2. Introduction| Methodology | Linear mixed-effects model | Discussion phonetic variation • Widely exists in spontaneous speech • Duration • Segmental realization • Pitch • Why?

3. Introduction| Methodology | Linear mixed-effects model | Discussion explaining variation Listener-oriented Talker-oriented Result of ease or difficulty of production Examples Shortening and reduction in High-frequency or high-predictability forms “articulatory routinization” (Bybee, 2001) • 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 Many word properties have the same predictions for comprehension and production…

4. Introduction| Methodology | Linear mixed-effects model | Discussion 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!

5. Introduction| Methodology | Linear mixed-effects model | Discussion phonological neighborhood density High-density words are hard for perception but easy for production (Dell & Gordon, 2003)

6. Introduction| Methodology | Linear mixed-effects model | Discussion 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) add fad Additional factors: neighborhood freq. cap cat fat coat fight kite

7. Introduction| Methodology | Linear mixed-effects model | Discussion 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

8. Introduction| Methodology | Linear mixed-effects model | Discussion phonological neighbors and word production • Facilitation • Words from dense neighborhoods induce fewer speech errors and have shorter latency times in picture naming tasks (Vitevitch 2002)

9. Introduction| Methodology | Linear mixed-effects model | Discussion 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? perception production

10. Introduction| Methodology | Linear mixed-effects model | Discussion 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)

11. Introduction| Methodology | Linear mixed-effects model | Discussion keywords of current study • Spontaneous speech • Aspects of production • Word duration • Vowel production • High-density words are shorter  talker-oriented

12. Introduction| Methodology | Linear mixed-effects model | Discussion data • Buckeye corpus (Pitt et al 2007) • 40 speakers, ~300,000 words • Target words • CVC • Monomorphemic • Content words • 414 word types / 13,858 tokens

13. Introduction| Methodology | Linear mixed-effects model | Discussion 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

14. Introduction| Methodology | Linear mixed-effects model | Discussion 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

15. Introduction| Methodology | Linear mixed-effects model | Discussion linear mixed-effects model • Fixed effects • All predictors • Neighborhood measures • Control variables • Random effects • Speaker • Word

16. Introduction| Methodology | Linear mixed-effects model | Discussion modeling results • Neighborhood density • A significant negative effect • More neighbors  shorter duration • Facilitation • Neighbor frequency • Insignificant

17. Introduction| Methodology | Linear mixed-effects model | Discussion partial effect of neighborhood density Effect confirmed by model evaluation.

18. Introduction| Methodology | Linear mixed-effects model | Discussion 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

19. Introduction| Methodology | Linear mixed-effects model | Discussion 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 dispersion Ease of articulation? Fast lexical access? Not really… Probably… Synchrony between planning and articulation (Bell et al, 2009)

20. Introduction| Methodology | Linear mixed-effects model | Discussion 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

21. Introduction| Methodology | Linear mixed-effects model | Discussion 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

22. Introduction| Methodology | Linear mixed-effects model | Discussion The end…

23. 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

24. Thanks to… • Prof. Susanne Gahl and Prof. Keith Johnson for helpful discussion • Anonymous subjects in Buckeye • Buckeye corpus developers

25. Introduction| Methodology | Linear mixed-effects model | Discussion Perception & Production Dell & Gordon (2003) Perception Production add fad cap cat fat coat fight kite

26. Introduction| Methodology | Linear mixed-effects model | Discussion model evaluation • Confirms the robustness of the results • Testing t-values • Model comparison • Cross-validation

27. Introduction| Methodology | Linear mixed-effects model | Discussion Individual differences Having one more neighbor decreases duration by 0.4%

28. Introduction| Methodology | Linear mixed-effects model | Discussion Distribution of neighborhood density and neighbor frequency