Crowdsourcing for Speech Processing: Applications to Data by Maxine Eskenazi

By Maxine Eskenazi

Provides an insightful and sensible advent to crowdsourcing as a method of quickly processing speech data

Intended in case you are looking to start within the area and  find out how to manage a role, what interfaces can be found, how one can check the paintings, and so forth. in addition to in the event you have already got used crowdsourcing and wish to create greater projects and acquire larger tests of the paintings of the gang. it is going to contain screenshots to teach examples of fine and bad interfaces; examples of case stories in speech processing initiatives, facing the duty construction technique, reviewing recommendations within the interface, within the collection of medium (MTurk or different) and explaining offerings, etc.

  • Provides an insightful and functional advent to crowdsourcing as a way of quickly processing speech data.
  • Addresses vital points of this new method that are meant to be mastered prior to making an attempt a crowdsourcing application.
  • Offers speech researchers the desire that they could spend less time facing the knowledge gathering/annotation bottleneck, leaving them to target the clinical issues. 
  • Readers will at once enjoy the book’s profitable examples of the way crowd- sourcing was once applied for speech processing, discussions of interface and processing offerings that labored and  offerings that didn’t, and guidance on the right way to play and list speech over the web, the right way to layout projects, and the way to evaluate workers.

Essential analyzing for researchers and practitioners in speech examine teams keen on speech processing

Chapter 1 an summary (pages 1–7): Maxine Eskenazi
Chapter 2 the fundamentals (pages 8–36): Maxine Eskenazi
Chapter three amassing Speech from Crowds (pages 37–71): Ian McGraw
Chapter four Crowdsourcing for Speech Transcription (pages 72–105): Gabriel Parent
Chapter five how one can keep watch over and make the most of Crowd?Collected Speech (pages 106–136): Ian McGraw and Joseph Polifroni
Chapter 6 an outline (pages 137–172): Martin Cooke, Jon Barker and Maria Luisa Garcia Lecumber
Chapter 7 Crowdsourced overview of Speech Synthesis (pages 173–216): Sabine Buchholz, Javier Latorre and Kayoko Yanagisawa
Chapter eight Crowdsourcing for Spoken conversation approach review (pages 217–240): Zhaojun Yang, Gina?Anne Levow and Helen Meng
Chapter nine Interfaces for Crowdsourcing structures (pages 241–279): Christoph Draxler
Chapter 10 Crowdsourcing for commercial Spoken conversation structures (pages 280–302): David Suendermann and Roberto Pieraccini
Chapter eleven fiscal and moral heritage of Crowdsourcing for Speech (pages 303–334): Gilles Adda, Joseph J. Mariani, Laurent Besacier and Hadrien Gelas

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Extra info for Crowdsourcing for Speech Processing: Applications to Data Collection, Transcription and Assessment

Sample text

Wolters M, Isaac K and Renals S (2010) Evaluating speech synthesis intelligibility using Amazon Mechanical Turk. Proceedings of 7th Speech Synthesis Workshop. WordWave International. uk (accessed 9 July 2012). Zaidan O and Callison-Burch C (2011) Crowdsourcing translation: professional quality from non-professionals. Proceedings of ACL-2011. Further reading Black AW, Bunnell HT, Dou Y, Muthukumar PK, Metze F, Perry D, Polzehl T, Prahallad K, Steidl S and Vaughn C (2012) Articulatory features for expressive speech synthesis.

An example of this type of quality control is the “gold-unit” in CrowdFlower: a requester defines gold-units, which CrowdFlower inserts in the task that the workers complete. The worker receives feedback about whether they are completing the task properly (thus reinforcing their understanding of the task). This online quality control also shows the crowd that quality matters and that they are being monitored. It may be that the predominant use of MTurk, where it is not as easy to automatically insert gold-unit type checks, is the reason that this process has not yet been widely adopted by the speech community.

In the case of speech acquisition: • Give instructions as to how to use the microphone and rely on the honesty of the worker. • Ask the speaker to listen to what was recorded and approve it. • Sample the recorded signal level in one or two utterances and give the worker feedback. For speech transcription: • Use an audio captcha or have the worker transcribe one or more aforeknown utterances. • Do not let the worker continue if a transcription is empty or was started before the end of the playback.

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