The Buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability

By Mark A. Pitt, Keith Johnson, Elizabeth Hume, Scott F. Kiesling, William D. Raymond. Speech Communication, 2005

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/d6f7accec31194582a353495cffe620a669a1578

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Citations in the corpus

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input (2021, methodology): “We used speech recordings from four corpora: two corpora of read news articles—a subset of the Wall Street Journal corpus of American English (83) (WSJ) and the Globalphone corpus of Japanese (84) (GPJ)— and two corpora of spontaneous speech—the Buckeye corpus of American English (85) (BUC) and a su”
Across-Language Perspective on Speech Information Rate (2011, background): “The psychology of learning and motivation: Advances in research and theory, vol. 8, ed. by Gordon H. Bower, 47–89.”
ABX-Discriminability Measures and Applications. (Mesures de discriminabilité ABX et applications) (2016, methodology): “The evaluation was carried out using recordings from two freely accessible corpora of continuous speech: the Buckeye Corpus of American English [99] and the NCHLT corpus of Xitsonga [100].”
What Determines Inter-Coder Agreement in Manual Annotations? A Meta-Analytic Investigation (2011, background): “Computational linguists have started to tackle this question (Oppermann, Burger, and Weilhammer 2000; Syrdal et al. 2001; Pitt et al. 2005).”
A segmental framework for fully-unsupervised large-vocabulary speech recognition (2016, background): “The ﬁrst two are disjoint subsets extracted from the Buckeye corpus of conversational English [49], while the third is a portion of the Xitsonga section of the NCHLT corpus of languages spoken in South Africa [27].”
A comparison of neural network methods for unsupervised representation learning on the zero resource speech challenge (2015, unknown): “The first is a 5 hour portion of the Buckeye corpus [5] distributed as part of the ZRSC.”
Commentary: transcript variations and the indexicality of transcribing practices (2007, background): “…a problem of inconsistency in the writings of the authors themselves (O’Connell and Kowal, 1990), a problem of reproduction, quotation, or editing of transcripts (Kitzinger, 1998), or a problem of reliability and intersubjectivity (Kerswill and Wright, 1990; Peräkyla, 1997; Pitt et al., 2005).”
Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation (2020, methodology): “We evaluate our method on the TIMIT [20] and Buckeye [21] datasets.”
Variation in transcription (2007, background): “Within both sociocultural linguistic scholarship and the language sciences more generally, an issue of enduring concern with respect to the transcription of spoken language is the reliability and validity of transcripts (e.g. Ferber, 1991; Kerswill and Wright, 1990; Lane et al., 1996; Patterson et a”
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (2019, methodology): “The English data are drawn from the Buckeye Corpus (Pitt et al., 2005) and contain 4h59m05s of conversational speech from 12 speakers.”