Abstract
In the past two decades, variation has received a lot of attention in mainstream generative phonology, and several different models have been developed to account for variable phonological phenomena. However, all existing generative models of phonological variation account for the overall rate at which some process applies in a corpus, and therefore implicitly assume that all words are affected equally by a variable process. In this paper, we show that this is not the case. Many variable phenomena are more likely to apply to frequent than to infrequent words. A model that accounts perfectly for the overall rate of application of some variable process therefore does not necessarily account very well for the actual application of the process to individual words. We illustrate this with two examples, English t/d-deletion and Japanese geminate devoicing. We then augment one existing generative model (noisy Harmonic Grammar) to incorporate the contribution of usage frequency to the application of variable processes. In this model, the influence of frequency is incorporated by scaling the weights of faithfulness constraints up or down for words of different frequencies. This augmented model accounts significantly better for variation than existing generative models.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Noisy HG was first implemented by Paul Boersma in Praat (Boersma and Weenink 2009) as early as 2006.
Throughout this paper, all logarithmic transformations use a base of 10. For instance, the word and has a CELEX frequency of 514,946, and hence a log frequency of log10(514,946)=5.71. In the Buckeye Corpus, and appears in pre-vocalic context 3,273 times, and in 2,966 of these occurrences its final /d/ was deleted. In this context, and therefore shows a deletion rate of 90.6 %. In the middle panel of Fig. 1, the data point that appears in the upper right-hand corner of the graph therefore corresponds to and at a log frequency of 5.71 on the x-axis, and at a deletion rate of 90.6 % on the y-axis.
See later in this section on why we use the beta distribution rather than a more well-known distribution such as the normal distribution.
We also leave open the possibility that the value of ρ can vary across different speech styles. A larger value for ρ results in a larger range for the beta distribution, and hence in modes that deviate more from zero. Since the mode of the beta distribution is used as the scaling factor in the evaluation of some word, a larger ρ (and hence more extreme mode and scaling factor) will increase the influence that frequency can have on the determination of H-scores. It is therefore possible that the value of ρ may fluctuate to account for speech situations in which frequency has a bigger or smaller impact. We do not explore this possibility further in this paper, however.
An Excel file for the calculation of the beta distribution’s mode under different settings of the three parameters is available from http://www.quantitativeskills.com/sisa/rojo/distribs.htm. In this file, the range parameter ρ is represented by A and B, with A=−ρ and B=ρ. The shape parameter α is represented by p, and β by q.
These data are simplified with regard to the pre-consonantal context. Labov (1989) and Guy (1991), among others, show that t/d-deletion rates are different before consonants of different types. We follow the practice in the vast majority of the t/d-deletion literature of lumping all of the consonants together.
A token was coded as “t/d deleted” if no segment was transcribed for the underlying t/d. In the Buckeye Corpus, underlying t/d was transcribed with several different surface realizations, including faithful realizations [t] or [d], glottalized realizations [
] or [
], flap [
], etc. All tokens transcribed with one of these realizations were coded as “t/d retained”. Since the corpus contains no articulatory data, deletion is defined here as the absence of any acoustic evidence of t/d. An actually articulated t/d might not have any acoustic realization when it is articulated before a labial consonant. If the labial closure of the following consonant is made before the release of the t/d, the potential acoustic effect of the coronal release is masked by the labial closure, and hence becomes inaudible (Browman and Goldstein 1990). The actual articulatory t/d-deletion rate before consonants may therefore be somewhat lower than the acoustic rate reported here. As a check of the potential influence that this acoustic masking could have on our data, we counted the number of tokens in our pre-consonantal category followed by labial and non-labial consonants. We found that more than 80 % of the pre-consonantal tokens appear before non-labial consonants.The coding conventions in the Buckeye Corpus do not actually include a category for pauses. We coded as pre-pausal the following tokens: (i) tokens where the corpus indicates that silence followed an utterance; (ii) tokens where the corpus indicates that an utterance was followed by the interviewer speaking, and where it was clear from the context that the interviewer did not interrupt the interviewee mid-utterance; (iii) utterances followed by some kind of non-speech vocalization noise, and where the context made it clear that this vocalization noise did not occur mid-utterance.
The corpus of t/d-words that we used is available as “supplementary material” on the Springer link for this article, or from the first author upon request.
Bybee (2001) and Jurafsky et al. (2001:252–255) show that mere lexical usage frequency does not capture the full influence of frequency. Just as important, and in some instances maybe even more important, is frequency of use within a specific syntagmatic context. That is, the [t] in best may delete more often from a more frequent phrase such as best friend than from a less frequent phrase such as best fruit. Although an adequate account of phonological variation will ultimately have to incorporate all relevant types of frequency influences (and all other relevant influences), we will focus only on lexical usage frequency in this article.
Since log of zero is undefined, a constant of one was added to all frequencies before they were log-transformed.
One could raise some concerns about using CELEX to measure usage frequency. First, CELEX is a British corpus, and usage frequency may differ between CELEX and the American speakers included in the Buckeye Corpus. Second, although CELEX includes some spoken sources, the majority of the frequency counts in CELEX come from written texts. Usage frequency may be different between spoken and written language.
A possibly more accurate measure of the usage frequency of words for the speakers who contributed to the Buckeye Corpus would be the Buckeye Corpus itself—i.e., just counting the frequency with which each token appears in the corpus. However, since the Buckeye Corpus is comparatively small, it does not differentiate well between words with low usage frequencies—many words appear only once in the corpus. Facing the same problem with regard to the Buckeye Corpus and CELEX, Raymond et al. (2006) showed that CELEX and Buckeye frequencies are highly correlated (r=0.82). In fact, using CELEX for frequency counts, even when dealing with American English, is standard practice in the field (Albright 2009; Coetzee 2005, 2008). We therefore follow the standard practice, using CELEX for frequency counts in our study.
The decision to use 23 frequency bins is to some extent arbitrary. A finer-grained division into more bins could potentially give a more detailed picture of how usage frequency interacts with deletion. However, relying on more bins also results in some bins containing too few data points to reliably calculate deletion rates. There is a trade-off between the reliability of the deletion rate for each frequency bin and the fine-grainedness with which the frequency range is sampled. We decided to use bins that contain at least 50 tokens each, resulting in the 23 bins used here.
On each of the three graphs, there is one data point with an extremely high log frequency, just below 6. This data point corresponds to the word and, which accounts for more than half of all the tokens in our corpus. If this data point is removed, the positive correlation between frequency and deletion rate remains, even if it is less strong (Pre-C: r 2=0.21, p<0.05; Pre-V: r 2=0.22, p<0.05; Pre-Pause: r 2=0.14, p<0.11). Due to the fact that extremely high frequency words such as and show much higher deletion rates, these words are often excluded from the data sets used in variationist sociolinguistic studies of t/d-deletion (Patrick 1992:172). By including frequency as a factor in our model, we do not have to exclude frequent words. Their seemingly anomalous behavior is no longer anomalous, but rather expected given the model that we develop.
The Praat input file is available as “supplementary material” on the Springer link for this article, or from the first author upon request.
In particular, the following settings were used: (i) The initial weights of all constraints were set to 100. Changing the initial weights may influence the speed of learning, but as long as sufficient learning time is allowed, it will not influence the final grammar that is learned; (ii) An evaluation noise of 2.0 was used. Changing the evaluation noise may influence the absolute difference in weight between constraints, but will not influence the eventual performance of the grammar; (iii) The initial plasticity was set to 1.0, with 4 decrements of 0.1 in plasticity at every 100,000 replications. As explained by Boersma and Hayes (2001) with regard to their GLA for stochastic OT, starting out with a higher initial plasticity results in faster initial learning. Decreasing plasticity later in learning results in more accurate frequency matching of the learning input. An equally good grammar could be learned by starting out with a small plasticity, but more learning time might be required.
For this production-oriented simulation, we also used Praat’s default settings: (i) An evaluation noise of 2.0 was used—the same value used during the learning simulation; (ii) Each input type (pre-consonantal, pre-vocalic and pre-pausal) was submitted to the grammar 100,000 times, and the frequency with which each output candidate (deletion or retention) was selected was tallied.
If the sum of a constraint’s weight and the noise added to this weight at a particular evaluation occasion is less than zero, Praat resets it to zero during evaluation. This adjustment prevents a candidate from being rewarded in its H-score for violating a constraint—a negative constraint weight multiplied by the negative integer used to mark constraint violation would have increased the H-score.
Using whole number increments for ρ is motivated by practical considerations. If smaller increments were used, it is possible that a slightly better fit could be achieved.
Mean square error is calculated according to the formula \(\sum^{n}_{i=1}(P_{i}-O_{i})^{2}\), where P i is the value predicted for observation i, and O i the observed value for observation i. This value is an overall index of the deviation between the model prediction and the actually observed data. Improvement relative to the baseline model is calculated by first determining the difference in mean square error between the baseline and the model being evaluated—this difference represents the improvement of the new model relative to the baseline in terms of mean square error. This difference is then converted into an improvement percentage. For instance, to determine the improvement of a model with ρ=5 relative to the baseline in (19), we first determine the difference in mean square error between the two models (i.e., 1009.7−208.2=801.5). We then convert this to a percentage (i.e. 801.5/1009.7×100=79.4 %).
Specifically, in addition to the linear transformation defined in (22), we also used an exponential and sigmoid transformation. The formulas used in these two transformations are given below. Under both of these transformations, the positive correlation between frequency of devoicing and usage frequency is preserved: exponential: r 2=0.34, p<0.01; sigmoid: r 2=0.41, p<0.01.
Let r be the average naturalness rating that some token t received, and devoice(t) the rate of devoicing in token t. Let norm r be the standardized value of r. Then:
The learning input file is available as “supplementary material” on the Springer link for this article, or from the first author upon request.
The high frequency of /bagudaddo/ in Amano and Kondo (2000) is a result of their frequency counts being taken from a corpus of newspapers including the time after the American invasion of Iraq. Although it is not clear that /bagudaddo/ will still have such a high frequency for the average Japanese speaker, we opted not to adjust its frequency for the purposes of this paper. The participants in Kawahara’s experiment were mostly university students who were probably familiar with this event, so that /bagudaddo/ would have had a high frequency for them. The fact that /bagudaddo/ pronounced with devoicing, i.e., as [bagudatto], received a high naturalness rating in Kawahara (2011a) suggests that this might be correct.
Following standard conventions in the literature on Japanese phonology, we use /j/ here for the affricate /dƷ/.
As explained in footnote 24, we also explored an exponential and sigmoid transformation of the naturalness ratings. Frequency scaled models for corpora based on these transformations also performed better than baseline models, although the improvement was slightly less good than what we found for the linear transformation reported in the text. Improvement of the frequency scaled model over the baseline model was as follows: exponential transformation = 49.0 %; sigmoid transformation = 42.1 %.
Since a process cannot apply at a rate of higher than 100 %, this statement has to be qualified. Imagine a grammar where pre-consonantal context has a base deletion rate of 80 % and pre-pausal context of 50 %. Deletion in pre-consonantal position can be increased by at most 20 % by the contribution of scaling factors. The same holds for scaling factors that reduce the application of a simplification process and the floor of application, 0 %.
] or [
], flap [
], etc. All tokens transcribed with one of these realizations were coded as “t/d retained”. Since the corpus contains no articulatory data, deletion is defined here as the absence of any acoustic evidence of t/d. An actually articulated t/d might not have any acoustic realization when it is articulated before a labial consonant. If the labial closure of the following consonant is made before the release of the t/d, the potential acoustic effect of the coronal release is masked by the labial closure, and hence becomes inaudible (Browman and Goldstein 
References
Akaike, Hirotugu. 1973. Information theory as an extension of the maximum likelihood principle. In Second international symposium on information theory, eds. Boris N. Petrov and Frigyes Csaki, 267–281. Budapest: Akademiai Kiado.
Akaike, Hirotugu. 1983. Information measures and model selection. International Statistical Institute 44: 277–291.
Albright, Adam. 2009. Feature-based generalisation as a source of gradient acceptability. Phonology 26: 9–41.
Amano, Shigeaki, and Tadahisa Kondo. 2000. NTT database series: Lexical properties of Japanese, 2nd release. Tokyo: Sanseido.
Anttila, Arto. 1997. Deriving variation from grammar. In Variation, change and phonological theory, eds. Frans Hinskens, Roeland van Hout, and Leo Wetzels, 35–68. Amsterdam: John Benjamins.
Anttila, Arto. 2002a. Morphologically conditioned phonological alternations. Natural Language & Linguistic Theory 20: 1–42.
Anttila, Arto. 2002b. Variation and phonological theory. In Handbook of language variation and change, eds. Jack K. Chambers, Peter Trudgill, and Natalie Schilling-Estes, 206–243. Oxford: Blackwell.
Anttila, Arto. 2006. Variation and opacity. Natural Language & Linguistic Theory 24: 893–944.
Anttila, Arto. 2007. Variation and optionality. In The Cambridge handbook of phonology, ed. Paul de Lacy, 519–536. Cambridge: Cambridge University Press.
Anttila, Arto, Vivienne Fong, Stefan Benus, and Jennifer Nycz. 2008. Variation and opacity in Singapore English consonant clusters. Phonology 25: 181–216.
Auger, Julie. 2001. Phonological variation and Optimality Theory: Evidence from word-initial vowel epenthesis in Vimeu Picard. Language Variation and Change 13: 253–303.
Baayen, R. Harald, Richard Piepenbrock, and Leon Gulikers. 1995. The CELEX Lexical Database (CD-ROM). Philadelphia: Linguistic Data Consortium.
Baese-Berk, Melissa, and Matt Goldrick. 2009. Mechanisms of interaction in speech production. Language and Cognitive Processes 24: 147–185.
Bane, Max. to appear. A combinatoric model of variation in the English dative alternation. In Proceedings of the 36th annual meeting of the Berkeley Linguistics Society. Berkeley: Berkeley Linguistics Society.
Bane, Max. 2011. Deriving the structure of variation from the structure of non-variation in the English dative. In Proceedings of the 28th annual meeting of the West Coast conference on formal linguistics, eds. Mary Byram Washburn, Katherine McKinney-Bock, Erika Varis, Ann Sawyer, and Barbara Tomaszewicz, 42–50. Somerville: Cascadilla Press.
Bayley, Robert. 1995. Consonant cluster reduction in Tejano English. Language Variation and Change 6: 303–326.
Bayley, Robert. 2002. The quantitative paradigm. In The handbook of language variation and change, eds. Jack K. Chambers, Peter Trudgill, and Natalie Schilling-Estes, 117–141. Oxford: Blackwell.
Bell, Alan, Jason Brenier, Michelle Gregory, Cynthia Girand, and Daniel Jurafsky. 2009. Predictability effects on durations of content and function words in conversational English. Journal of Memory and Language 60: 92–111.
Benua, Laura. 2000. Phonological relations between words. New York: Garland.
Boersma, Paul. 1997. How we learn variation, optionality, and probability. University of Amsterdam Institute of Phonetic Sciences Proceedings 21: 43–58.
Boersma, Paul. 2008. Emergent ranking of faithfulness explains markedness and licensing by cue. Ms., University of Amsterdam. www.fon.hum.uva.nl/paul/papers/EmergeFaith.pdf. Accessed 11 July 2012.
Boersma, Paul, and Bruce Hayes. 2001. Empirical tests of the Gradual Learning Algorithm. Linguistic Inquiry 32: 45–86.
Boersma, Paul, and Joe Pater. 2008. Convergence properties of a Gradual Learning Algorithm for Harmonic Grammar. Ms., University of Amsterdam and University of Massachusetts, Amherst. www.fon.hum.uva.nl/paul/papers/boersmaPaterHGGLA.pdf. Accessed 11 July 2012.
Boersma, Paul, and David Weenink. 2009. Praat: Doing phonetics by computer version 5.1.20. [Computer Program.] http://www.praat.org. 31 Accessed October 2009.
Browman, Catherine P., and Louis Goldstein. 1990. Tiers in articulatory phonology, with some implications for casual speech. In Papers in laboratory phonology I: Between the grammar and physics of speech, eds. John Kingston and Mary E. Beckman, 341–376. Cambridge: Cambridge University Press.
Burnham, Kenneth P., and David R. Anderson. 2004. Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research 33: 261–304.
Bybee, Joan L. 1985. Morphology: A study of the relation between meaning and form. Amsterdam: Benjamins.
Bybee, Joan L. 2000. The phonology of the lexicon: Evidence from lexical diffusion. In Usage-based models of language, eds. Michael Barlow and Suzanne Kemmer, 65–85. Stanford: CSLI.
Bybee, Joan L. 2001. Phonology and language use. Cambridge: Cambridge University Press.
Bybee, Joan L. 2002. Word frequency and context of use in lexical diffusion of phonetically conditioned sound change. Language Variation and Change 14: 261–290.
Bybee, Joan L. 2006. From usage to grammar: the mind’s response to repetition. Language 82: 711–733.
Bybee, Joan L. 2007. Frequency of use and the organization of language. Oxford: Oxford University Press.
Byrd, Dani. 1992. A note on English sentence-final stops. In UCLA Working Papers in Phonetics, Vol. 81, eds. Pat Keating and Dani Byrd, 37–38. Los Angeles: Department of Linguistics, UCLA.
Carpenter, Angela. 2006. Acquisition of a natural versus an unnatural stress system. Ph.D. diss., University of Massachusetts.
Carpenter, Angela. 2010. A naturalness bias in learning stress. Phonology 27: 345–392.
Coetzee, Andries W. 2004. What it means to be a loser: Non-optimal candidates in Optimality Theory. Ph.D. diss., University of Massachusetts.
Coetzee, Andries W. 2005. The Obligatory Contour Principle in the perception of English. In Prosodies, eds. Sónia Frota, Marina Vigário, and Maria João Frietas, 223–245. Berlin: Mouton de Gruyter.
Coetzee, Andries W. 2006. Variation as accessing “non-optimal” candidates. Phonology 23: 337–385.
Coetzee, Andries W. 2008. Grammaticality and ungrammaticality in phonology. Language 84: 218–257.
Coetzee, Andries W. 2009a. An integrated grammatical/non-grammatical model of phonological variation. In Current issues in linguistic interfaces: Volume 2, eds. Young-Se Kang, Jong-Yurl Yoon, Hyunkyung Yo, Sze-Wing Tang, Yong-Soon Kang, Youngjun Jang, Chul Kim, Kyoung-Ae Kim, and Hye-Kyung Kang, 267–294. Seoul: Hankookmunhwasa.
Coetzee, Andries W. 2009b. Learning lexical indexation. Phonology 26: 109–145.
Coetzee, Andries W. 2009c. Phonological variation and lexical frequency. In NELS 38, Vol. 1, eds. Anisa Schardl, Martin Walkow, and Muhammad Abdurrahman, 189–202. Amherst: GLSA.
Coetzee, Andries W. 2012. Variation: Where laboratory and theoretical phonology meet. In Oxford handbook of laboratory phonology, eds. Abigail C. Cohn, Cécile Fougeron, and Marie K. Huffman, 62–75. Oxford: Oxford University Press.
Coetzee, Andries W., and Joe Pater. 2008. Weighted constraints and gradient restrictions on place co-occurrence in Muna and Arabic. Natural Language & Linguistic Theory 26: 289–337.
Coetzee, Andries W., and Joe Pater. 2011. The place of variation in phonological theory. In Handbook of phonological theory: 2nd Edition, eds. John Goldsmith, Jason Riggle, and Alan Yu, 401–434. Cambridge: Blackwell.
Coetzee, Andries W., and Rigardt Pretorius. 2010. Phonetically grounded phonology and sound change: The case of Tswana labial plosives. Journal of Phonetics 38: 404–421.
Côté, Marie-Hélène. 2004. Syntagmatic distinctness in consonant deletion. Phonology 21: 1–41.
Crawford, Clifford James. 2009. Adaptation and transmission in Japanese loanword phonology. Ph.D. diss., Cornell University.
Diehl, Randy L., and Margaret A. Walsh. 1989. An auditory basis for the stimulus-length effect in the perception of stops and glides. The Journal of the Acoustical Society of America 85: 2154–2164.
Eek, Arvo, and Einar Meister. 1995. The perception of stop consonants: locus equations and spectral integration. In ICPhS XIII: Proceedings of the XIIIth International Congress of Phonetic Sciences, Stockholm, Sweden, Vol. 1, 18–21.
Fasold, R. 1972. Tense marking in Black English. Arlington: Center for Applied Linguistics.
File-Muriel, Richard J. 2010. Lexical frequency as a scalar variable in explaining variation. The Canadian Journal of Linguistics 55: 1–25.
Fowler, Carol A. 1994. Invariants, specifiers, cues: An investigation of locus equations as information for place of articulation. Perception and Psychophysics 55: 597–610.
Frank, Austin F., and T. Florian Jaeger. 2008. Speaking rationally: Uniform Information Density as an optimal strategy for language production. In Proceedings of the 30th annual meeting of the cognitive science society, eds. Brad C. Love, Ken McRae, and Vladimir M. Sloutsky, 939–944. Austin: Cognitive Science Society.
Fruchter, David, and Harvey M. Sussman. 1997. The perceptual relevance of locus equations. The Journal of the Acoustical Society of America 102: 2997–3008.
Gahl, Susanne. 2008. Time and thyme are not homophones: The effect of lemma frequency on word durations in spontaneous speech. Language 84: 474–496.
Gahl, Susanne, and Alan Yu, eds. 2006. Special issue on exemplar-based models in linguistics. Vol. 23(3) of The Linguistic Review. Berlin: De Gruyter Mouton.
Goeman, Ton. 1999. T-deletie in Nederlandse dialecten. Kwantitatieve analyse van structurele, ruimtelijke en temporele variate. Ph.D. diss., Vrije Universiteit, Amsterdam. The Hague: Holland Academic Graphics.
Goeman, Ton, and Pieter van Reenen. 1985. Word-final t-deletion in Dutch dialects. The roles of conceptual prominence, articulatory complexity, paradigmatic properties, token frequency and geographical distribution. Amsterdam: Vakgroep Algemene Taalwetenschap Vrije Universiteit.
Goldinger, Stephen D., Paul A. Luce, David B. Pisoni, and Joanne K. Marcario. 1992. Form-based priming in spoken word recognition: The roles of competition and bias. Journal of Experimental Psychology. Learning, Memory, and Cognition 18: 1211–1238.
Goldsmith, John. 1993. Harmonic phonology. In The last phonological rule: Reflections on constraints and derivations, ed. John Goldsmith, 21–60. Chicago: Chicago University Press.
Goldsmith, John, ed. 1995. The handbook of phonological theory. Oxford: Blackwell.
Gupta, Arjun K., and Saralees Nadarajah, eds. 2004. Handbook of the beta distribution and its applications. New York: Marcel Dekker.
Guy, Gregory R. 1991. Explanation in variable phonology: An exponential model of morphological constraints. Language Variation and Change 3: 1–22.
Guy, Gregory R. 2011. Variability. In The Blackwell companion to phonology: Volume 4, eds. Marc van Oostendorp, Colin J. Ewen, Elizabeth Hume, and Keren Rice, 2190–2213. Oxford: Blackwell.
Guy, Gregory R., and Charles Boberg. 1997. Inherent variability and the Obligatory Contour Principle. Language Variation and Change 9: 149–164.
Hammond, Michael. 1994. An OT account of variability in Walmatjari stress. Ms., University of Arizona.
Hayes, Bruce, and Zsuzsa Cziráky Londe. 2006. Stochastic phonological knowledge: The case of Hungarian vowel harmony. Phonology 23: 59–104.
Hooper, Joan B. 1976. Word frequency in lexical diffusion and the source of morphological change. In Current progress in historical linguistics, ed. William M. Christie, 95–105. Amsterdam: North-Holland.
Hyman, Larry. 2001. On the limits of phonetic determinism in phonology: *NC revisited. In The role of speech perception phenomena in phonology, eds. Elizabeth Hume and Keith Johnson, 141–185. New York: Academic Press.
Itô, Junko. 1988. Syllable theory in prosodic phonology. New York: Garland.
Itô, Junko, and Armin Mester. 2001. Covert generalizations in Optimality Theory: The role of stratal faithfulness constraints. Studies in Phonetics, Phonology, and Morphology 7: 273–299.
Jaeger, T. Florian. 2010. Redundancy and reduction: Speakers manage syntactic information density. Cognitive Psychology 61: 23–62.
Jesney, Karen. 2007. The locus of variation in weighted constraint grammars. Poster presented at the Workshop on Variation, Gradience and Frequency in Phonology. Stanford University, July 2007 [Downloaded on December 27, 2007 from http://people.umass.edu/kjesney/papers.html].
Jurafsky, Daniel, Alan Bell, Michelle Gregory, and William D. Raymond. 2001. Probabilistic relations between words: Evidence from reduction in lexical production. In Frequency and the emergence of linguistic structure, eds. Joan L. Bybee and Paul Hopper, 229–254. Amsterdam: Benjamins.
Kaisse, Ellen M., and Patricia A. Shaw. 1985. On the theory of lexical phonology. Phonology Yearbook 2: 1–30.
Kaneko, Emiko, and Gregory K. Iverson. 2009. Phonetic and other factors in Japanese on-line adaptation of English final consonants. In Papers from the eighth annual conference of the Japanese Society for Language Science, eds. Shunji Inagaki and Makiko Hirakawa, Vol. 8 of Studies in language sciences, 179–185. Tokyo: Kuroshio Publications.
Kang, Hye-Kyung. 1994. Variation in past-marking and the question of the system in Trinidadian English. In CLS 30: Papers from the 30th regional meeting of the Chicago Linguistic Society Volume 2: The parasession on variation in linguistic theory, ed. Katherine Beals, 15–164. Chicago: Chicago Linguistic Society.
Katayama, Motoko. 1998. Optimality Theory and Japanese loanword phonology. Ph.D. diss., University of California, Santa Cruz.
Kawahara, Shigeto. 2005. Voicing and geminacy in Japanese: An acoustic and perceptual study. In Univeristy of Massachusetts occasional papers in linguistics, Vol. 31, eds. Katherine Flack and Shigeto Kawahara, 87–120. Amherst: GLSA.
Kawahara, Shigeto. 2006. A faithfulness ranking projected from a perceptibility scale: The case of [+voice] in Japanese. Language 82: 536–574.
Kawahara, Shigeto. 2008. Phonetic naturalness and unnaturalness in Japanese loanword phonology. Journal of East Asian Linguistics 18: 317–330.
Kawahara, Shigeto. 2011a. Aspects of Japanese loanword devoicing. Journal of East Asian Linguistics 20: 169–194.
Kawahara, Shigeto. 2011b. Japanese loanword devoicing revisited: A wellformedness rating study. Natural Language & Linguistic Theory 29: 705–723.
Kempen, Gerard, and Karin Harbusch. 2008. Comparing linguistic judgments and corpus frequencies as windows on grammatical competence: A study of argument linearization in German clauses. In The discourse potential of underspecified structures, ed. Anita Steube, 179–192. Berlin: Walter de Gruyter.
Kewley-Port, Diane. 1983. Time-varying features as correlates of place of articulation in stop consonants. The Journal of the Acoustical Society of America 73: 322–335.
Kewley-Port, Diane, David Pisoni, and Michael Studdert-Kennedy. 1983. Perception of static and dynamic acoustic cues to place of articulation in initial stop consonants. The Journal of the Acoustical Society of America 73: 1779–1793.
Kiparsky, Paul. 1985. Some consequences of Lexical Phonology. Phonology Yearbook 2: 85–138.
Kiparsky, Paul. 1993. An OT perspective on phonological variation. Ms. Stanford University. Paper presented at the Rutgers Optimality Workshop. October, 1993. http://www.stanford.edu/~kiparsky/Papers/nwave94.pdf. Accessed 11 July 2012.
Labov, William. 1966. The Social stratification of English in New York City. Washington: Center for Applied Linguistics.
Labov, William. 1969. Contraction, deletion, and inherent variability of the English copula. Language 45: 715–762.
Labov, William. 1989. The child as linguistic historian. Language Variation and Change 1: 85–97.
Labov, William. 2004. Quantitative analysis of linguistic variation. In Sociolinguistics: An international handbook of the science of language and society, volume 1: 2nd edition, eds. Ulrich Ammon, Norbert Dittmar, Klaus J. Mattheier, and Peter Trudgill, 6–21. Berlin: Mouton de Gruyter.
Lacoste, Véronique. 2008. Learning the sounds of Standard Jamaican English: Variationist, phonological and pedagogical perspectives on 7-year-old children’s classroom speech. Ph.D. diss., University of Essex.
Lahiri, Aditi, Letitia Gewirth, and Sheila E. Blumstein. 1984. A reconsideration of acoustic invariance for place of articulation in diffuse stop consonants: Evidence from a cross-language study. The Journal of the Acoustical Society of America 76: 391–404.
Legendre, Géraldine, Yoshiro Miyata, and Paul Smolensky. 1990. Can connectionism contribute to syntax? Harmonic Grammar, with an application. In Proceedings of the 26th regional meeting of the Chicago Linguistic Society, eds. Michael Ziolkowski, Manuela Noske, and Karen Deaton, 237–252. Chicago: Chicago Linguistic Society.
Lin, Susan, Patrice S. Beddor, and Andries W. Coetzee. 2011. Gestural reduction and sound change: An ultrasound study. In ICPhS XVII: Proceedings of the 17th International Congress of Phonetic Sciences, eds. Wai-Sum Lee and Eric Zee, 1250–1253.
Lindblom, Bjorn. 1990. Explaining phonetic variation: A sketch of the H and H theory. In Speech production and speech modeling, eds. William J. Hardcastle and Alain Marchal, 403–439. Dordrecht: Kluwer Academic.
Luce, Paul A., and David B. Pisoni. 1986. Recognizing spoken words: The neighborhood activation model. Ear and Hearing 19: 1–35.
Martínez-Celdrán, Eugenio, and Xavier Villalba. 1995. Locus equations as a metric for place of articulation in automatic speech recognition. In ICPhS XIII: Proceedings of the XIIIth International Congress of Phonetic Sciences, Stockholm, Sweden, Vol. 1, eds. Kjell Elenius and Peter Branderud, 30–33. Stockholm: Stockholm University.
Malécot, André. 1958. The role of releases in the identification of released final stops: A series of tape-cutting experiments. Language 34: 274–284.
McCarthy, John J. 2003. Sympathy, cumulativity, and the Duke-of-York gambit. In The syllable in Optimality Theory, eds. Caroline Féry and Ruben van de Vijver, 23–76. Cambridge: Cambridge University Press.
McCarthy, John J., and Alan Prince. 1995. Faithfulness and reduplicative identity. In Papers in Optimality Theory, eds. Jill Beckman, Suzanne Urbanczyk, and Laura Walsh Dickey. Vol. 18 of University of Massachusetts occasional papers in linguistics, 249–384. Amherst: GLSA.
McNamara, Timothy P. 2005. Semantic priming: Perspectives from memory and word recognition. New York: Psychology Press, Taylor and Francis.
Merchant, Nazarré, and Bruce Tesar. 2005. Learning underlying forms by searching restricted lexical subspaces. In CLS 41: Proceedings from the 41st annual meeting of the Chicago Linguistic Society, Vol. 2, eds. Rodney L. Edwards, Patrick J. Midtlyng, Colin L. Sprague, and Kjersti G. Stensrud, 33–48. Chicago: CLS.
Mitterer, Holger, and Mirjam Ernestus. 2006. Listeners recover /t/’s that speakers reduce: Evidence from /t/-lenition in Dutch. Journal of Phonetics 34: 73–103.
Moreton, Elliott. 2008. Learning bias as a factor in phonological typology. Phonology 25: 83–127.
Moreton, Elliott. 2010. Underphonologization and modularity bias. In Phonological argumentation: Essays on evidence and motivation, ed. Stephen Parker, 79–101. London: Equinox.
Nearey, Terrance M., and Sherrie E. Shammas. 1987. Formant transitions as partly distinctive invariant properties in the identification of voiced stops. Canadian Acoustics 15: 17–24.
Nevins, Andrew. 2007. Review of Scheer 2004. Lingua 118: 425–434.
Nishimura, Kohei. 2003. Lyman’s law in loanwords. M.A. thesis, Nagoya University.
Nishimura, Kohei. 2006. Lyman’s Law in loanwords. Phonological Studies [Onin Kenkyuu] 9: 83–90.
Pater, Joe. 2009. Weighted constraints in generative linguistics. Cognitive Science 33: 999–1035.
Pater, Joe, and Anne-Michelle Tessier. 2006. L1 phonotactic knowledge and the L2 acquisition of alternations. In Inquiries in linguistic development: Studies in honor of Lydia White, eds. Roumyana Slabakova, Silvina A. Montrul, and Philippe Prévost, 115–131. Amsterdam: Benjamins.
Patrick, Peter L. 1992. Creoles at the intersection of variable processes: t,d deletion and past-marking in the Jamaican mesolect. Language Variation and Change 3: 171–189.
Patterson, David, and Cynthia M. Connine. 2001. Variant frequency in flap production: A corpus analysis of variant frequency in American English flap production. Phonetica 58: 254–275.
Phillips, Betty S. 1984. Word frequency and the actuation of sound change. Language 60: 320–342.
Phillips, Betty S. 2001. Lexical diffusion, lexical frequency, and lexical analysis. In Frequency and emergence of linguistic structure, eds. Joan L. Bybee and Paul Hopper, 123–136. Amsterdam: John Benjamins.
Phillips, Betty S. 2006. Word frequency and lexical diffusion. New York: Palgrave Macmillan.
Pierrehumbert, Janet. 2001. Exemplar dynamics: Word frequency, lenition and contrast. In Frequency effects and the emergence of lexical structure, eds. Joan L. Bybee and Paul Hopper, 137–157. Amsterdam: Benjamins.
Pitt, Mark A., Laura Dilley, Keith Johnson, Scott Kiesling, William D. Raymond, Elizabeth Hume, and E. Fosler-Lussier. 2007. Buckeye corpus of conversational speech, 2nd Release. Columbus: Department of Psychology, Ohio State University. www.buckeyecorpus.osu.edu.
Postal, Paul. 1966. Review of “Elements of general linguistics” by André Martinet. Foundations of Language 2: 151–186.
Postal, Paul. 1968. Aspects of phonological theory. New York: Harper and Row.
Prince, Alan, and Paul Smolensky. 1993. Optimality Theory: Constraint interaction in Generative Grammar. Ms., New Brunswick, Rutgers University.
Prince, Alan, and Paul Smolensky. 2004. Optimality Theory: Constraint interaction in Generative Grammar. Oxford: Blackwell.
Pullum, Geoffrey K. 2003. Learnability. In The international encyclopedia of linguistics, ed. William J. Frawley, 431–434. Oxford: Oxford University Press.
Raymond, William D., Robin Dautricourt, and Elizabeth Hume. 2006. Word-medial /t,d/ deletion in spontaneous speech: Modeling the effects of extra-linguistic, lexical, and phonological factors. Language Variation and Change 18: 55–97.
Reynolds, Bill. 1994. Variation and phonological theory. Ph.D. diss., University of Pennsylvania.
Santa Ana, Otto. 1991. Phonetic simplification processes in the English of the barrio: A cross-generational sociolinguistic study of the Chicanos of Los Angeles. Ph.D. diss., University of Pennsylvania.
Scarborough, Rebecca. 2004. Coarticulation and the structure of the lexicon. Ph.D. diss., UCLA.
Scarborough, Rebecca. 2010. Lexical and contextual predictability: Confluent effects on the production of vowels. In Papers in laboratory phonology X: Variation, phonetic detail and phonological modeling, eds. Cécile Fougeron, Barbara Kühnert, Mariapaola D’Imperio, and Nathalie Vallée, 557–586. Berlin: Mouton de Gruyter.
Schouten, Marten E.H. 1982. T-deletie in de stad Utrecht: Schoolkinderen en grootouders. Forum der Letteren 23: 282–291.
Schouten, Marten E.H. 1984. T-deletie in Het Zuiden van die provincie Utrecht. Taal en Tongval 36: 162–173.
Smolensky, Paul, and Géraldine Legendre, eds. 2006. The harmonic mind: From neural computation to Optimality-Theoretic grammar, Volume 1: Cognitive architecture, Volume 2: Linguistic and philosophical implications. Cambridge: MIT Press.
Steriade, Donca. 1999. Phonetics in phonology: The case of laryngeal neutralization. In UCLA working papers in linguistics 2 (Papers in phonology 3), ed. Matthew K. Gordon, 25–146. Los Angeles: Department of Linguistics, UCLA.
Steriade, Donca. 2001. Directional asymmetries in place assimilation. In The role of speech perception in phonology, eds. Elizabeth Hume and Keith Johnson, 219–250. San Diego: Academic Press.
Stevens, Kenneth N., and Sheila E. Blumstein. 1978. Invariant cues for place of articulation in stop consonants. The Journal of the Acoustical Society of America 64: 1358–1368.
Stevens, Kenneth N., and Samuel J. Keyser. 1989. Primary features and their enhancement in consonants. Language 65: 81–106.
Sussman, Harvey M., Helen A. McCaffrey, and Sandar A. Matthews. 1991. An investigation of locus equations as a source of relational invariance for stop place categorization. The Journal of the Acoustical Society of America 90: 936–946.
Tanaka, Shin-Ichi. 2009. The eurhythmics of segmental melody. Journal of the Phonetic Society of Japan 13: 44–52.
Tesar, Bruce. 2006. Learning from paradigmatic information. In NELS 36: Proceedings of the thirty-sixth annual meeting of the North East Linguistic Society, Vol. 2, eds. Christopher Davis, Amy-Rose Deal, and Youri Zabbal, 619–638. Amherst: GLSA.
Tesar, Bruce, and Paul Smolensky. 1996. Learnability in Optimality Theory (long version). Technical report JHU-CogSci-96-4, Department of Cognitive Science, The Johns Hopkins University, Baltimore, Md.
Tesar, Bruce, and Paul Smolensky. 1998. Learnability in Optimality Theory. Linguistic Inquiry 29: 229–268.
van Oostendorp, Marc. 1997. Style levels in conflict resolution. In Variation, change and phonological theory, eds. Frans Hinskens, Roeland van Hout, and Leo Wetzels, 207–229. Amsterdam: John Benjamins.
Versace, Rémy, and Brigitte Nevers. 2003. Word frequency effect on repetition priming as a function of prime duration and delay between the prime and the target. British Journal of Psychology 94: 389–408.
Vitevitch, Michael S., and Paul A. Luce. 1998. When words compete: Levels of processing in spoken word recognition. Psychological Science 9: 325–329.
Vitevitch, Michael S., and Paul A. Luce. 1999. Probabilistic phonotactics and neighborhood activation in spoken word recognition. Journal of Memory and Language 40: 374–408.
Walsh, Margaret A., and Randy L. Diehl. 1991. Formant transition duration and amplitude rise time as cues to the stop/glide distinction. The Quarterly Journal of Experimental Psychology A 43: 603–620.
Zsiga, Elisabeth. 2000. Phonetic alignment constraints: Consonant overlap and palatalization in English and Russian. Journal of Phonetics 28: 69–102.
Acknowledgements
The ideas expressed in this paper were presented in various forms at NAPhC 5, NELS 38, NELS 41, the University of Michigan, the University of Massachusetts, Michigan State University, Stanford University, and SUNY Stony Brook. The feedback and reaction of the audiences at these events contributed significantly to the development of our thoughts. This work has also been discussed in detail with many individuals, and we acknowledge our gratitude for their contribution. This list includes Joe Pater, John McCarthy, John Kingston, Anne-Michelle Tessier, Pam Beddor, San Duanmu, Ricardo Bermúdez-Otero, William Labov, Paul Smolensky, Matt Goldrick, Colin Wilson, Kevin McGowan, and Susan Lin. We also acknowledge the help of Amelia Compton in running many of the Praat simulations in this paper. The three reviewers and the associate editor similarly helped us to improve the paper and to express our ideas more clearly. As always, any remaining errors and shortcomings are our own.
Author information
Authors and Affiliations
Corresponding author
Electronic Supplementary Material
Below are the links to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Coetzee, A.W., Kawahara, S. Frequency biases in phonological variation. Nat Lang Linguist Theory 31, 47–89 (2013). https://doi.org/10.1007/s11049-012-9179-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11049-012-9179-z