The Workshop on Usage-based Approaches to Phonological Change, Us(e)Phon, will take place on July 5, 2020, 9 AM – 12 PM Pacific, 10 AM – 1 PM Mountain, 12 PM – 3 PM Eastern, 4 PM – 7 PM Greenwich. the day before the Laboratory Phonology conference (LabPhon). It will be on Zoom (link coming later). All are welcome to attend but you do need to pre-register (for free) using this link so that we know to send you the password for the Zoom meeting.
If you have any question, please contact the organizers, Vsevolod (Volya) Kapatsinski (vkapatsi@uoregon.edu) and Corrine Occhino (cmocchino@gmail.com)
This workshop is proudly sponsored by the University of Oregon Department of Linguistics, a major center for usage-based and experimental linguistics, which has provided funds for sign language interpreting, and by the Rochester Institute of Technology, which has provided funds and personnel for close captioning.
Speaker line-up
(times are Pacific)
9-9:10 PT Vsevolod Kapatsinski (University of Oregon) & Corrine Occhino (Rochester Institute of Technology) — Opening remarks
9:15-9:30 PT Janet Pierrehumbert (University of Oxford) — Lexical gang effects and the force of analogy over time
9:35-9:50 PT Rory Turnbull (University of Hawai’i, Mānoa / Newcastle University) — Predictability effects and natural selection
9:55-10:10 PT Fabian Tomaschek (University of Tübingen) and Frederik Hartmann (University of Konstanz) — How German words changed during 700 years due to frequency of occurrence and paradigmatic and lexical discriminability
10:10-10:30 Discussion
10:30-10:45 Break
10:45-11:00 PT Susanne Gahl (University of California, Berkeley) — Which age-related changes in pronunciation are lexical? And why don’t we already know?
11:05-11:20 PT Esther Brown (University of Colorado, Boulder) — Lexical frequency effects in words’ production rates: Operating independently or expressing an accumulation of contextual conditioning factors?
11:25-11:40 PT Joan Bybee (University of New Mexico) — Joint innovation: An integrated model of sound change
11:40-12:00 Discussion
12:00-12:30 Optional business meeting
Registration
To receive the Zoom password and updates about the workshop, please register here. Registration is free.
Please register before 5 PM Pacific on July 4. We will (re-)send out zoom room links at that time.
Abstracts
Esther Brown (University of Colorado, Boulder) — Lexical frequency effects in words’ production rates: Operating independently or expressing an accumulation of contextual conditioning factors?
Studies investigating variation in speech seek to consider linguistic, extralinguistic and/or discourse~pragmatic factors operating upon the target form of interest, because these predictors constrain the variation in anticipated ways. Usage-based research has determined that these forms, which reflect the probabilistic conditioning of the factors of the production context, become registered in memory as variant forms of words and/or constructions (Bybee 2001). The factors constraining variation, therefore, that form the basis of myriad studies of phonological variation and change, can be understood to not only have an online effect in the production context (favoring or disfavoring, for instance, a reduced form), but to also have a complimentary cumulative effect on variable forms (Bybee 2002).
Nevertheless, words and constructions differ significantly with regard to their exposure to conditioning factors of the discourse context (Brown 2013). That is, opportunity biases arise naturally in use whereby some words co-occur with specific conditioning factors significantly more than others. High frequency words (compared to low frequency words), for example, are predictable (Jurafsky et al 2001), are less informative (Cohen Priva & Gleason 2018, Seyfarth 2014), populate dense phonological neighborhoods (Gahl & Strand 2016), and benefit from enhanced lexical access and articulatory routinization (Bybee 2001), all of which predict faster target rate articulations. Other factors constraining target rates include repeated mentions (Kahn & Arnold 2015), syntactic diversity (Lester, Baum & Biron 2018), proximity to pause (Sóskuthy and Hay 2018), stylistic variation (Bailey 2019), and words’ history of use in discourse contexts of differing speech rates (Brown & Raymond under review). Is it the case that high frequency words (as a class) are used proportionally more often in such discourse contexts conditioning fast speech? This work explores to what extent word frequency is capturing via correlation online conditioning factors specific to high (vs. low) frequency words.
References
Bailey, G. (2019). Ki(ng) in the North: effects of duration, boundary and pause on post-nasal [ɡ]-presence. Laboratory Phonology.
Brown, E. L. (2013). Word classes in studies of phonological variation: Conditioning factors or epiphenomena. In C. Howe, S. Blackwell, & M. Lubbers Quesada (Eds.). Selected Proceedings of the 15th Hispanic Linguistics Symposium (pp. 179-186). Summerville, MA: Cascadilla Proceedings Project.
Brown, E. L. & Raymond, W. D. (under review). [Title]
Bybee, J. (2001). Phonology and language use. Cambridge, MA: Cambridge University Press.
Bybee, J. (2002). Word frequency and context of use in the lexical diffusion of phonetically conditioned sound change. Language Variation and Change, 14(3), 261-290.
Cohen Priva, U., & Gleason, E. (2018). The role of fast speech in sound change. Paper presented at the Annual Conference of the Cognitive Science Society. Madison, WI. July, 2018.
Gahl, S., Yao, Y., & Johnson, K. (2012). Why reduce? Phonological neighborhood density and phonetic reduction in spontaneous speech. Journal of Memory and Language, 66(4), 789-806.
Gahl, S., & Strand, J. F. (2016). Many neighborhoods: Phonological and perceptual neighborhood density in lexical production and perception. Journal of Memory and Language, 89, 162-178.
Jurafsky, D., Bell, A., Gregory, M, & Raymond, W, D. (2001). The effect of language model probability on pronunciation reduction. Paper presented at the International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2001, Salt Lake City, UT.
Kahn, J. M., & Arnold, J. E. (2015). Articulatory and lexical repetition effects on durational reduction: Speaker experience vs. common ground. Language, Cognition and Neuroscience, 30(1-2), 103-119. Lester, N. A., Baum, D., & Biron, T. (2018). Phonetic duration of nouns depends on de-lexicalized syntactic distributions: Evidence from naturally occurring conversation. Proceedings of the 40th Annual Conference of the Cognitive Science Society.
Joan Bybee (University of New Mexico) — Joint innovation: An integrated model of sound change
A widely-adopted model of sound change postulates a two-step process on the analogy of biological evolution: variation and selection (Lindblom et al. 1995) or altered replication + selection (Croft 2000). As Stevens and Harrington 2014 put it: ‘An ongoing challenge in sound change research is to link the initiation of sound change within individual cognitive grammars to the diffusion of novel variants through the community’.
In this paper I propose that the link between individuals and community is much tighter than the two-step model assumes, and most sound change occurs, not in isolated individual cognitive grammars, but rather in the joint activity of constructing a conversation.
First, note that speech is highly practiced skilled behavior. Second, within a speech community, phonetic productions of different individuals are very similar, so much so that we recognize dialect membership quite easily. Where there is variation, individual speakers have overlapping ranges of phonetic variation. It follows, then, that speakers within a community have very similar motor patterns and further that these motor patterns would respond in similar ways to hypo- and hyper-articulation.
Third, the uses of hypo- and hyper-articulation are complex and involve knowledge of the probabilities of a word occurring in a particular context (Bell et al. 2009), the knowledge shared by speaker and listener (Lindblom et al. 1990), the prosodic groupings the speaker wants to make (Barth-Weingarten and Couper-Kuhlen2011) whether the word has been mentioned before (Fowler and Housum 1987) and the construction the word occurs in (Bybee and Napoleão de Souza 2019). All this detailed phonetic knowledge applies to both production and perception.
Fourth, participants in a conversation are engaged in a cooperative activity. Conversational analysis reveals many instances in which participants finish one another’s sentences, share syntactic constructions (Ono and Thompson 1996) and create a ‘chorus’ of co-production (Lerner 2002). These studies suggest that listeners follow very closely the segmental and prosodic details of the speaker and know how to interpret variation.
From these points I argue that the micro-changes that initiate and lead to sound change are likely shared across conversational participants. Such changes are usually reductive; they move in small increments across contexts, lexical items and language users, as is consistent with what is known about the gradualness of sound change. Because they emerge from shared articulatory patterns, they are phonetically consistent across speakers. Because they respond to the physical aspects of the vocal tract and general properties of the motor system, they are also similar across languages and show a clear directionality. For all these reasons, the innovations that result in sound change emerge from trends that are common to a whole community of language users so that innovation and spread are not two distinct steps in the process.
References
Barth-Weingarten, Dagmar, and Elizabeth Couper-Kuhlen. 2011. “Action, Prosody and Emergetn Constructions: The Case of And.” In In P. Auer and S. Pfänder (Eds.) Constructions: Emerging and Emergent, 263–92. Berlin: DeGruyter.
Bell, Alan, Jason Brenier, Michelle Gregory, Cynthia Girand, and Dan Jurafsky. 2009. “Predictability Effects on Durations of Content and Function Words in Conversational English.” Journal of Memory and Language 60, no. 1:92–111.
Bybee, Joan, and Ricardo Napoleão de Souza. 2019. “Vowel Duration in English Adjectives in Attributive and Predicative Constructions.” Language and Cognition, https://doi.org/doi:10.1017/langcog.2019.3.
Croft, William. 2000. Explaining Language Change. Harlow, England: Longman Linguistic Library.
Fowler, Carol A., and Jonathan Housum. 1987. “Talkers’ Signaling of ‘New’ and ‘Old’ Words in Speech and Listeners’ Perception and Use of the Distinction.” Journal of Memory and Language 26: 489–504.
Lerner, Gene. 2002. “Turn-Sharing. The Choral Co-Production of Talk-in-Interaction.” In C. E. Ford, B. Fox and S. A. Thompson (Eds) The Language of Turn and Sequence, 225–56. Oxford: Oxford University Press.
Lindblom, Björn. “Explaining Phonetic Variation: A Sketch of the H&H Theory.” 1990. In W.J. Hardcastle and A. Marchal (Eds.), Speech Production and Speech Modelling, 403–39. Dordrecht: Kluwer Academic Publishers.
Lindblom, Björn, Susan Guion, and Susan Hura, Seung-Jae Moon and Raquel Willerman. 1995. “Is Sound Change Adaptive?” Rivista Di Linguistica 7, no. 1: 5–37.
Ono, T, and S. A. Thompson. 1996. “Interaction and Syntax in the Structure of Conversational Discourse: Collaboration, Overlap and Syntactic Dissociation.” In E. Hovey and D. Scott (Eds), Computational and Conversational Discourse, 67–96. Berlin: Springer.
Stevens, Mary, and Jonathan Harrington. 2014. “The Individual and the Actuation of Sound Change.” Loquens 1, no. 1: 1–10. http://dx.doi.org/10.3989/loquens.2014.003.
Susanne Gahl (Dept. of Linguistics, UC Berkeley) — Which age-related changes in pronunciation are lexical? And why don’t we already know?
As talkers get older, their pronunciation changes. Many studies of age-related change in pronunciation have focused on effects of (chronological, physiological, and cognitive) ageing as reflected in across-the-board changes, analogous to regular sound change. However, changes in pronunciation over the adult life of a talker can also be lexically specific.
Documenting across-the-board vs. lexically-specific age-related changes is complicated by the fact that estimates of usage frequency are usually age-aggregated. Such estimates overestimate usage frequency in some talkers and underestimate it in others.
In this talk, I review some evidence of lexically-specific changes in pronunciation over the adult life span, as well as the distortion of frequency effects due to different frequency measures.
Janet B Pierrehumbert (Dept. of Engineering Science, University of Oxford) — Lexical gang effects and the force of analogy over time
Traditional scholarship on language sound change distinguished regular sound changes (grounded in the phonetics) from analogical changes (grounded in the structure of the mental lexicon.) Detailed empirical studies in usage-based phonology have revealed that this putative dichotomy is not as strict as originally imagined. Nonetheless, the distinction between phonetic factors and analogical factors in sound change remains. A hallmark of analogical factors in sound change are lexical gang effects. These are productive generalizations based on similarities amongst stored words in form and meaning. In wug experiments, they are manifested in the competition between different exponents for the same inflectional category. In historical change, they induce trans-paradigmatic leveling. Irregular forms often fall into line with the dominant pattern, but very importantly, regular forms may also fall into line with a tight gang of exceptional forms. Related effects are found in derivational morphology in connection with the productivity of different affixes, the growth of morphological families, and the competition amongst forms that are nearly synonymous.
Quantifying lexical gang effects is critical for generating predictions about their consequences over time. This talk will provide a quick tour of the successes to date in addressing this need, including some pointers to quantifying semantic similarity. It will also formulate questions that remain to be answered.
Fabian Tomaschek (University of Tübingen) and Frederik Hartmann (University of Konstanz) — How German words changed during 700 years due to frequency of occurrence and paradigmatic and lexical discriminability
Several lexical sources of synchronic and diachronic sound change have been identified. The first is a word’s frequency of occurrence which has been shown to be both, a driver of sound change, as it increases the probability of reductive sound change [1, 2, 3], and an anchor for older forms in analogical change [4, 5]. The second is phonological neighborhood density, a measure of a word’s discriminability, which has been shown to be inversely proportional to the probability of contrast merger [3, 6]. In this way the forces shaping the lexicon avoid to create homophones [7], i.e. a case of zero phonetic discriminability between two semantic contrasts.
So far, sound change studies have focused on unique phones. However, it is unlikely that sound change is restricted to one phone per word. The contrary is more probable. In the current study, we therefore investigated to how these sources affect the shape of whole words – concretely, how verbs from Middle High German (MHG) have changed to today’s standard variety of New High German (NHG). The amount of change between MHG and NHG was gauged by means of Levenshtein Distance between phonetic transcriptions. As predictors we used the verb’s frequency of occurrence and its phonological neighborhood density (PND) in MHG. Furthermore, given that neighborhood density captures only direct neighbors, we furthermore assessed a verb’s mean phonological similarity (MPD), calculated by the average Levenshtein distance to other words. To obtain the MHG measures, we specifically compiled a new digital corpus. In light of systematic phonetic variability due to paradigmatic relations [8, 9, 10, 11], we assessed these measures within individual verbal paradigms or towards the remaining lexicon. Finally, all the lexical measures were assessed for NHG, too.
We used Supervised Components Generalized Linear Regression (SCGLR, [12]) for our investigation, a regression technique based on principal components, that allows to fit multiple dependent variables with strongly correlated predictors. We found a negative correlation between the amount of sound change within a word form and its word frequency in MHG, supporting the finding of frequency of occurrence as an anchor for older forms. Furthermore, words with more phonological neighbors and smaller mean phonological distances, i.e. less discriminable words, underwent fewer changes between MHG and NHG than words with fewer phonological neighbors and larger mean phonological distances, i.e. more discriminable words. Crucially, measures calculated for the paradigm and the remaining lexicon had independent effects.
The present results indicate that a word’s discriminability depends on its relation to verbs within the same paradigm and as well as to the entire lexicon. Furthermore, sound change due to discriminability and frequency of occurrence is not restricted to unique phones. Rather, these forces shape the entire word form.
References
[1] Joan Bybee. Word frequency and context of use in the lexical diffusion of phonetically conditioned sound change. Language Variation and Change, 14(3):261–290, 2002.
[2] M. Aylett and A. Turk. Language redundancy predicts syllabic duration and the spectral characteristics of vocalic syllable nuclei. Journal of the Acoustical Society of America, 119(5):3048–3058, 2006.
[3] A. Wedel, A. Kaplan, and S. Jackson. High functional load inhibits phonological contrast loss: A corpus study. Cognition, 128(2):179 – 186, 2013.
[4] Jennifer B Hay, Janet B Pierrehumbert, Abby J Walker, and Patrick LaShell. Tracking word frequency effects through 130 years of sound change. Cognition, 139:83–91, 2015.
[5] Simon Todd, Janet B Pierrehumbert, and Jennifer Hay. Word frequency effects in sound change as a consequence of perceptual asymmetries: An exemplar-based model. Cognition, 185:1–20, 2019.
[6] A. Wedel, S. Jackson, and A. Kaplan. Functional load and the lexicon: Evidence that syntactic category and frequency relationships in minimal lemma pairs predict the loss of phoneme contrasts in language change. Language and Speech, 56(3):395–417, 2013.
[7] Juliette Blevins. Evolutionary phonology: A holistic approach to sound change typology. In Patrick Honeybone and Joseph Curtis Salmons, editors, The Oxford handbook of historical phonology, 485–500. Oxford University Press, Oxford, 2015.
[8] F. Tomaschek, I. Plag, M. Ernestus, and R. H. Baayen. Phonetic effects of morphology and context: Modeling the duration of word-final s in english with na¨ıve discriminative learning. Journal of Linguistics, 2018.
[9] V. Kuperman, M. Pluymaekers, M. Ernestus, and H. Baayen. Morphological predictability and acoustic salience of interfixes in Dutch compounds. JASA, 121:2261–2271, 2007.
[10] Claire Cohen. Combining structure and usage patterns in morpheme production: Probabilistic effects of sentence context and inflectional paradigms. PhD dissertation, University of California, Berkeley, 2014.
[11] Clara Cohen. Probabilistic reduction and probabilistic enhancement. Morphology, 24(4):291–323, 2014.
[12] X. Bry, C. Trottier, T. Verron, and F. Mortier. Supervised component generalized linear regression using a pls-extension of the fisher scoring algorithm. Journal of Multivariate Analysis, 119:47 – 60, 2013.
Rory Turnbull (University of Hawai’i, Mānoa / Newcastle University) — Predictability effects and natural selection
Abstract:
Phonetic reduction is pervasive in natural speech. Previous research has found robust relationships between phonetic reduction and linguistic predictability, such that high-frequency words and words which are predictable from context tend to be phonetically reduced (Aylett & Turk, 2004; Gahl & Garnsey, 2004).
Many theoretical treatments of this phenomenon can be classified as either “talker-oriented” or “listener-oriented” (see Clopper & Turnbull 2018 for review). The talker-oriented accounts posit that these effects enhance talker ease, with various aspects of the architecture of speech production (both cognitive and physical) conspiring to lead to reduction for predictable elements. The listener-oriented accounts posit that these effects are instead for the benefit of the listener – by effectively enhancing contextually unpredictable items, these effects enhance communicative success.
However, a less-well explored possibility is that these effects arise as a consequence of the perception-production loop within an exemplar model of speech. Under such an account, individual speech tokens exist in memory and exert an influence on production and perception. A preponderance of reduced tokens, for instance, will lead to reduced productions. Crucially, reduced tokens are harder to perceive out of context than unreduced tokens (Ernestus et al., 2002; Tucker, 2011). Reduced tokens thus must rely on contextual support in order for them to be accurately perceived and incorporated into the exemplar space. This process of “natural selection”, as proposed by, among others, Pierrehumbert (2001) and Silverman (2012), leads to a situation where reduction is licensed for high-frequency words but not low-frequency words.
The conceptual basis for this explanation is clear. However, so far this effect has not been implemented in a computational model. In fact, several existing computational implementations of exemplar theory inadvertently predict the opposite effect. This paper examines and expands these models, particularly Harrington & Schiel’s (2017) agent-based implementation of exemplar theory.
This paper further proposes simple modifications to existing models that demonstrate how frequency of use can lead to phonetic variation with minimal mechanisms. Phonetic reduction is essentially directional variation; to induce reduction it is necessary to encode biomechanical constraints into the model – in other words, some notion of phonetic effort, which the model seeks to minimize.
These models demonstrate that the “natural selection” approach to predictability effects is plausible and can be implemented computationally. By reducing phonetic reduction to consequences of natural selection over speech exemplars, these models provide a new perspective on the relationship between speech and predictability.
References
Aylett, M. and Turk, A. E. (2004). The smooth signal redundancy hypothesis: A functional explanation for relationships between redundancy, prosodic prominence, and duration in spontaneous speech. Language and Speech, 47(1):31–56.
Clopper, C. G., & Turnbull, R. (2018). Exploring variation in phonetic reduction: Linguistic, social, and cognitive factors. In Cangemi, F., Clayards, M., Niebuhr, O., Schuppler, B., & Zellers, M.(eds.), Rethinking reduction: Interdisciplinary perspectives on conditions, mechanisms, and domains for phonetic variation. Berlin: Mouton de Gruyter.
Ernestus, M., Baayen, R. H., and Schreuder, R. (2002). The recognition of reduced word forms. Brain and Language, 81:162–173.
Gahl, S. and Garnsey, S. M. (2004). Knowledge of grammar, knowledge of usage: Syntactic probabilities affect pronunciation variation. Language, 80(4):748–775.
Harrington, J., and Schiel, F. (2017). /u/-fronting and agent-based modeling: The relationship between the origin and spread of sound change. Language, 93(2): 414-445.
Pierrehumbert, J. (2001). Exemplar dynamics: word frequency, lenition and contrast. In Bybee, J. and Hopper, P., (eds.), Frequency and the emergence of linguistic structure, pages 137–158. John Benjamins, Amsterdam.
Silverman, D. (2012). Neutralization. Cambridge University Press, Cambridge.
Tucker, B. V. (2011). The effects of reduction on the processing of flaps and /g/ in isolated words. Journal of Phonetics, 39:312–318