Abigail C.Cohn, CécileFougeron, MarieHuffman (eds.). The Oxford Handbook of Laboratory Phonology. Oxford, U.K.: University Press. 2012. 863 pp. Hb (9780199575039) $175.00.

2013 ◽  
Vol 17 (3) ◽  
pp. 400-402
Author(s):  
Molly Babel
Keyword(s):  
Laboratory Phonology

Laboratory phonology 1

2017 ◽  
pp. 504-529
Author(s):  
Abigail C. Cohn ◽  
Cécile Fougeron ◽  
Marie K. Huffman
Keyword(s):  
Laboratory Phonology

Laboratory Phonology 7 (review)

Language ◽  
2004 ◽  
Vol 80 (1) ◽  
pp. 175-176
Author(s):  
Esther Herrera Zendejas
Keyword(s):  
Laboratory Phonology

scholarly journals Models of dataset size, question design, and cross-language speech perception for speech crowdsourcing applications

2015 ◽  
Vol 6 (3-4) ◽  
Author(s):  
Mark Hasegawa-Johnson ◽  
Jennifer Cole ◽  
Preethi Jyothi ◽  
Lav R. Varshney
Keyword(s):  
Estimation Error ◽  
Error Correcting Code ◽  
Theoretical Description ◽  
Error Correcting Codes ◽  
New Methods ◽  
Laboratory Phonology ◽  
Dataset Size ◽  
Phoneme Perception ◽  
Cross Language ◽  
The Relationship

AbstractTranscribers make mistakes. Workers recruited in a crowdsourcing marketplace, because of their varying levels of commitment and education, make more mistakes than workers in a controlled laboratory setting. Methods for compensating transcriber mistakes are desirable because, with such methods available, crowdsourcing has the potential to significantly increase the scale of experiments in laboratory phonology. This paper provides a brief tutorial on statistical learning theory, introducing the relationship between dataset size and estimation error, then presents a theoretical description and preliminary results for two new methods that control labeler error in laboratory phonology experiments. First, we discuss the method of crowdsourcing over error-correcting codes. In the error-correcting-code method, each difficult labeling task is first factored, by the experimenter, into the product of several easy labeling tasks (typically binary). Factoring increases the total number of tasks, nevertheless it results in faster completion and higher accuracy, because workers unable to perform the difficult task may be able to meaningfully contribute to the solution of each easy task. Second, we discuss the use of explicit mathematical models of the errors made by a worker in the crowd. In particular, we introduce the method of mismatched crowdsourcing, in which workers transcribe a language they do not understand, and an explicit mathematical model of second-language phoneme perception is used to learn and then compensate their transcription errors. Though introduced as technologies that increase the scale of phonology experiments, both methods have implications beyond increased scale. The method of easy questions permits us to probe the perception, by untrained listeners, of complicated phonological models; examples are provided from the prosody of English and Hindi. The method of mismatched crowdsourcing permits us to probe, in more detail than ever before, the perception of phonetic categories by listeners with a different phonological system.

Phonology and Phonetic Evidence: Papers in Laboratory Phonology IV

Language ◽  
1997 ◽  
Vol 73 (4) ◽  
pp. 908
Author(s):  
Stefan Frisch ◽  
Bruce Connell ◽  
Amalia Arvaniti
Keyword(s):  
Laboratory Phonology

PAPERS IN LABORATORY PHONOLOGY, VOL. V: ACQUISITION AND THE LEXICON.Michael B. Broe & Janet B. Pierrehumbert (Eds.). New York: Cambridge University Press, 2000. Pp. xiii + 400. $64.95 cloth.

2001 ◽  
Vol 23 (4) ◽  
pp. 565-566
Author(s):  
Debra M. Hardison
Keyword(s):  
New York ◽  
Empirical Data ◽  
Cambridge University ◽  
Laboratory Phonology ◽  
Human Capability

This volume extends the traditional focus of this series on the phonology-phonetics interface with papers exploring the contribution of various types of empirical data—including acoustic, articulatory, and perceptual—to the principal themes embodied in the questions of what constitutes a possible word and what underlies the human capability to produce language.

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