Updated: March 29, 2012.
News: Transcriptions can now be entered to calculate IPhOD measures and
list all phonological neighbors, based on the same counts and data
as version 2.0.
Try out the new calculator!
If you experience any issues with the calculator or search pages, please contact:
The Irvine Phonotactic Online Dictionary (IPhOD) is a large collection of English words and
pseudowords developed that was originally developed at UC Irvine for research on speech perception and production.
The collection allows researchers to select items for experiments, based on measures related to speech sounds. Specifically,
it can be used to answer questions such as: Which contains more unusual sound-sequences, dog or cat?
Which sounds like fewer other English words?
What are some nonsense words with similar phonological qualities?
All of the IPhOD tools on this website are freely available for academic and personal use.
There is also an blog to provide a forum for feedback,
questions, and suggestions - or use email to contact: Kenny Vaden.
Please cite IPhOD in the following manner (either as version 1.4 or 2.0):
Vaden, K.I., Halpin, H.R., Hickok, G.S. (2009). Irvine Phonotactic Online Dictionary, Version 2.0. [Data file].
Available from http://www.iphod.com.
A growing body of evidence demonstrates that we segment (Saffran et al., 1996), respond (Vitevitch et al., 1999),
produce (Vitevitch et al., 2004), and remember (Majerus et al., 2004) speech in ways that are affected by different kinds of
phonological frequency information. However, this speech research is restricted by the limited number of pronunciation collections
or utilities, which often derive their estimates narrowly (for instance, using small word collections) or present limited measurement
choices. While some collections, notably CELEX, address some of these concerns, they use British stress and pronunciation, which is
suboptimal for speech research using American English trained subjects. The Phonotactic
Probability Calculator (Vitevitch & Luce, 2004) uses American English pronunciations to compute position-specific phonotactic
probabilities, but provides no density estimates or frequency weighting options. Despite growing interest in phonotactic information,
it remains unavailable or difficult to derive for novel hypotheses with contemporary tools.
The current version (2.0) of the Irvine Phonotactic Online Dictionary (IPhOD) is a collection of phonotactic estimates
calculated across a broad sample to enable precise verbal stimuli selection for speech research and application in cognitive science,
computational linguistics, and natural language processing. IPhOD contains phonotactic and density estimates, American English
transcriptions of 1-28 phonemes, and word frequencies for 54030 word and 814840 pseudoword entries. Pseudowords are defined here as
word-like transcriptions consisting entirely of phoneme-pairs from real English words. Pseudowords like these are used in computational
psycholinguistics to study non-semantic language processes, since they have little meaning or association but are consistent enough with
a language to sound like typical words. The collection is freely available to download or search online.
Each IPhOD entry contains an American English phonetic transcription from the Carnegie-Mellon Pronouncing Dictionary (Weide, 1994),
and written word frequencies from the SUBTLEXus database (Brysbaert & New, 2009). Neighborhood density and word averaged phoneme-sequence
probabilities were extrapolated from those data using the same formulas for words and pseudowords, so that entries of either type could
be chosen using identical criteria. IPhOD is calculated broadly, over the entire word set in calculations for phonotactic probability and
neighborhood density, after the approach of Vitevitch and Luce (1999).
Phonotactic probabilities refer to the relative frequency for the sound sequences that are present in a given word. The
phonotactic measures in IPhOD extend upon definitions from Vitevitch and Luce (1999), elaborated upon below. The database was calculated
with two versions of each measure - one that treats vowel sounds identically regardless of syllable stress and a second version that
differentiates among vowels with different syllable stress. The syllable stressed counts were produced to investigate the extent to which
sensitivity to phoneme-order includes syllable stress. Distinguishing vowels by syllable stress transforms the small number of English vowel
sounds (compared to consonants) into a larger number of compartments, which changes the probability space of positions and sequences,
and also changes density counts.
Accessing IPhOD: downloads, searches, calculator
The IPhOD can be used in one of several ways. First, it may be useful to find words or pseudowords with values
in a specific range. For example, what pseudowords have between 20 and 25 phonological neighbors?
A second approach is to determine what the values are for specified words or pseudowords, for
example: what are the word frequencies for cat, dog, tree, car? We have developed several ways to access the information
in IPhOD, depending on your goal.
The IPhOD database can be downloaded in its entirety (text files) from the
download page. These files can be
opened using most available spreadsheet programs, or custom PERL scripts. A second option is to
search the database online, by
entering value ranges or word lists to obtain results. Finally, there is an
online calculator that produces phonotactic and
density values for lists of phonemic transcriptions that are entered by the user. An advantage of the latter two approaches is
that you can specify which output fields to include in results, and leave out columns that are not of interest. The online
calculator is helpful for generating values for words or pseudowords that are not included in the IPhOD database, and also
can list the phonological neighbors of each input transcription.
The IPhOD was developed by Kenny Vaden advised by Greg Hickok in the Department of Cognitive Sciences, UC Irvine.
We gratefully acknowledge the contributions of Harry Halpin (Informatics, University of Edinburgh) for the original XML markup and
online search functions, as well as the various contributions of Jean-Claude Falmagne, Kai Okada, Yasmine Omidvar and Corica Rodgers
(UC Irvine) for their contributions.
Published works citing IPhOD include:
Vaden, K.I., Kuchinsky, S.E., Keren, N.I., Harris, K.C., Ahlstrom, J.B., Dubno, J.R., Eckert, M.A. (2011). Inferior frontal sensitivity to common speech sounds is amplified by increasing word intelligibility. Neuropsychologia, 49(13), 3563-3572. [PMID: 21925521]
Vaden, K.I., Piquado, T., Hickok, G. (2011). Sublexical properties of spoken words modulate activity in Broca’s area but not superior temporal cortex: implications for models of speech recognition. Journal of Cognitive Neuroscience, 23(10), 2665-2674. [PMID: 21261450]
Flinker, A., Chang, E.F., Barbaro, N.M., Berger, M.S., Knight, R.T. (2011). Sub-centimeter language organization in the human temporal lobe. Brain and Language, 117(3),103-109.
Freedman, S.E., Barlow, J.A. (2011). Using whole-word production measures to determine the influence of phonotactic probability and neighborhood density on bilingual speech production. International Journal of Bilingualism,doi: 10.1177/1367006911425815.
Kuchinsky, S.E., Vaden, K.I., Keren, N.I., Harris, K.C., Ahlstrom, J.B., Dubno, J.R., Eckert, M.A. (In Press). Word intelligibility and age predict visual cortex activity during word listening. Cerebral Cortex. Accepted July 12, 2011. [PMID: 21862447]
de Lacey, P., Kingston, J. (In Press). Synchronic explanation. Natural Language and Linguistic Theory. Online at: http://www.pauldelacy.net/webpage/docs/delacy-kingston-2011-synchronic_explanation_prepub.pdf
Desai, R., Binder, J.R., Conant, L.L., Seidenberg, M.S. (2010). Activation of sensory-motor areas by sentences. Cerebral Cortex, 20(2), 468-478.
Chen, H.C., Vaid, J., Boas, D.A., Bortfeld, H. (2010). Examining the phonological neighborhood density effect using near infrared spectroscopy. Human Brain Mapping, accessible online since August 5, 2010. DOI: 10.1002/hbm.21115.
Contini, C. (2010). Lexical and sublexical characteristics of words produced disfluently by adults who stutter. Thesis, University of South Carolina, Columbia, SC. (Scholar Commons, http://scholarcommons.sc.edu/etd/326).
Chen, H.C. (2007). Mapping orthographic and phonological neighborhood density effects in visual word recognition in two distinct orthographies. Doctoral Thesis, Texas A&M University, College Station, TX. (Texas A&M Repository, URI: http://hdl.handle.net/1969.1/ETD-TAMU-1365)
Creel, S.C., Aslin, R.N., Tanenhaus, M.K. (2008). Heeding the voice of experience: the role of talker variation in lexical access. Cognition, 106, 633-664.
Sabri, M., Binder, J.R., Desai, R., Medler, D.A., Leitl, M.D., Libenthal, E. (2008). Attentional and linguistic interactions in speech perception. NeuroImage, 39(3), 1444-1456.
Vaden, K.I. (2009). Phonological processes in speech perception. Doctoral dissertation, University of California at Irvine, Irvine, CA. (Proquest document id: 1781083831, http://proquest.umi.com; ISBN: 9781109155495).
Vaden, K., Hickok, G. (2009). Sublexical and lexical processing in temporal and frontal lobes during word recognition. Society for Neuroscience, Chicago, IL.
Vaden, K., Muftuler, L.T., Hickok, G. (2010). Phonological repetition-suppression in bilateral superior temporal sulci. NeuroImage, 49(1), 1018-1023.
Brysbaert, M. and New, B. 2009. Moving beyond Kucera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English. Behavior Research Methods, 41, 997-990.
Griffin, Zenzi M. and Bock, Kathryn. 1998. Constraint, Word Frequency, and the Relationship between Lexical Processing Levels in Spoken Word Production. Journal of Memory and Language, 38(3), 313-338.
Hulme, C.; Roodenrys, S.; Schweickert, R.; Brown, G.D.A.; Martin, S.; and Stuart, G. 1997. Wordfrequency effects on short-term memory tasks. Journal of Experimental Psychology - Learning, Memory and Cognition, 23(5), 1217-1232.
Kucera, Henry and Francis, W. Nelson. 1967. Computational analysis of present-day American English. Providence, Brown University Press.
Majerus, Steve; Van der Linden, Martial; Mulder, Ludivine; Meulemans, Thierry; and Peters, Frederic 2004. Verbal short-term memory reflects the sublexical organization of the phonological language network. Journal of Memory and Language, 51(2), 297-306.
Saffran, Jenny R.; Newport, Elissa L.; and Aslin, Richard N. 1996. Word Segmentation: The Role of Distributional Cues. Journal of Memory and Language, 35(4), 606-621.
Vitevitch, Michael S.; Armbruster, Jonna; and Chu, Shinying. 2004. Sublexical and Lexical Representations in Speech Production: Effects of Phonotactic Probability and Onset Density. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(2), 514-529.
Vitevitch, Michael S.; Luce, Paul A.; Pisoni, David B.; and Auer, Edward T. 1999. Phonotactics, Neighborhood Activation, and Lexical Access for Spoken Words. Brain and Language, 68(1), 306-311.
Vitevitch, Michael S. and Luce, Paul A. 2004. A Web-based interface to calculate phonotactic probability for words and nonwords in English. Behavior Research Methods, Instruments, & Computers 36 (3), 481-487.
Weide, Robert L. 1994. CMU Pronouncing Dictionary. http://www.speech.cs.cmu.edu/cgi-bin/cmudict.
Wilson, M. 1988. MRC Psycholinguistic Database: Machine Readable Dictionary, Version 2. Behavioural Research Methods, Instruments & Computers, 20(1), 6-11. http://www.psy.uwa.edu.au/mrcdatabase/uwa_mrc.htm