An Iterative Dual Pathway Structure for Speech-to-Text Transcription

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An Iterative Dual Pathway Structure for Speech-to-Text Transcription

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Title: An Iterative Dual Pathway Structure for Speech-to-Text Transcription
Author: Chen, Yiling; Liem, Beatrice; Zhang, Haoqi

Note: Order does not necessarily reflect citation order of authors.

Citation: Liem, Beatrice, Haoqi Zhang, and Yiling Chen. Forthcoming. An iterative dual pathway structure for speech-to-text transcription. In Human Computation: Papers from the AAAI Workshop (WS-11-11). San Francisco, CA, August 2011, ed. Luis von Ahn and Panagiotis Ipeirotis. Association for the Advancement of Artificial Intelligence.
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Abstract: In this paper, we develop a new human computation algorithm for speech-to-text transcription that can potentially achieve the high accuracy of professional transcription using only microtasks deployed via an online task market or a game. The algorithm partitions audio clips into short 10-second segments for independent processing and joins adjacent outputs to produce the full transcription. Each segment is sent through an iterative dual pathway structure that allows participants in either path to iteratively refine the transcriptions of others in their path while being rewarded based on transcriptions in the other path, eliminating the need to check transcripts in a separate process. Initial experiments with local subjects show that produced transcripts are on average 96.6% accurate.
Other Sources: http://www.aaai.org/ocs/index.php/WS/AAAIW11/paper/view/3902
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:5142121

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  • FAS Scholarly Articles [7106]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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