Yao LSA Separating speaker- and listener- oriented forces in speech – Evidence from phonological neighborhood density

phonetic variation Introduction | 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 –F–Foreigner- and child-directed speech –S–Speech under noise –S–Shortening and reduction in High-frequency or high- predictability forms Talker-oriented Result of ease or difficulty of production Examples –S–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. fat fad fightkite cap add cat coat 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 perception production 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 –W–Word duration –V–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 dispersion Fast 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, Munson & Solomon (2004) The effect of phonological neighborhood on vowel articulation. JSLHR, 47, Pitt et al (2007Buckeye Corpus of Conversational Speech (2nd release) [ 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, Wright (1997) Lexical competition and reduction in speech: A preliminary report.. Research on Spoken Language Processing Progress Report. 21, 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 fightkite cap add cat coat 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 differences Introduction | 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