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2019

Disfluencies and Human Speech Transcription Errors

Zayats, Vicky, Tran, Trang, Wright, Richard et al.

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This paper explores contexts associated with errors in transcrip-tion of spontaneous speech, shedding light on human perceptionof disfluencies and other conversational speech phenomena.

Anew version of the Switchboard corpus is provided with disfluency annotations for careful speech transcripts, together with results showing the impact of transcription errors on evaluation of automatic disfluency detection.

Disfluencies and Human Speech Transcription Errors · Around

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J. J. Godfrey, E. C. Holliman, and J. McDaniel, “Switchboard: Telephone speech corpus for research and development,” in
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R. P. Lippmann, “Speech recognition by machines and humans,”
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N. Deshmukh, A. Gleeson, J. Picone, A. Ganapathiraju, and J. Hamaker, “Resegmentation of SWITCHBOARD,” in
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M. P. Marcus, B. Santorini, M. A. Marcinkiewicz, and A. Taylor, “Treebank-3,” tech. rep., Linguistic Data Consortium, 1999
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A. Stolcke, “SRILM – an extensible language modeling toolkit,” in
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Then

G. Saon, G. Kurata, T. Sercu, K. Audhkhasi, S. Thomas, D. Dimitriadis, X. Cui, B. Ramabhadran, M. Picheny, L.-L. Lim,
Original
2017
Later among the works it cites.
A. Stolcke and J. Droppo, “Comparing human and machine errors in conversational speech transcription,”
Original
2017
Later among the works it cites.
PhD thesis, Université Sorbonne Paris Cité; University of Washington, 2017
E. Le Grézause, · 2017
Later among the works it cites.
V. Zayats and M. Ostendorf, “Robust cross-domain disfluency detection with pattern match networks,”
Original
2018
Later among the works it cites.
T. Tran, S. Toshniwal, M. Bansal, K. Gimpel, K. Livescu, and M. Ostendorf, “Parsing speech: a neural approach to integrating lexical and acoustic-prosodic information,” in
Original

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W. Xiong, J. Droppo, X. Huang, F. Seide, M. L. Seltzer, A. Stolcke, D. Yu, and G. Zweig, “Toward human parity in conversational speech recognition,”

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