Download Crowdsourcing for Speech Processing: Applications to Data by Maxine Eskenazi PDF

By Maxine Eskenazi

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

Intended if you happen to are looking to start within the area and  how to manage a role, what interfaces can be found, tips on how to determine the paintings, and so forth. in addition to if you have already got used crowdsourcing and need to create higher initiatives and acquire greater checks of the paintings of the group. it is going to comprise screenshots to teach examples of excellent and negative interfaces; examples of case stories in speech processing projects, dealing with the duty production strategy, reviewing suggestions within the interface, within the number of medium (MTurk or different) and explaining offerings, etc.

  • Provides an insightful and sensible advent to crowdsourcing as a way of swiftly processing speech data.
  • Addresses vital features of this new procedure that are supposed to be mastered sooner than trying a crowdsourcing application.
  • Offers speech researchers the wish that they could spend less time facing the information gathering/annotation bottleneck, leaving them to target the medical issues. 
  • Readers will at once enjoy the book’s profitable examples of ways crowd- sourcing used to be carried out for speech processing, discussions of interface and processing offerings that labored and  offerings that didn’t, and instructions on tips on how to play and checklist speech over the net, how you can layout projects, and the way to evaluate workers.

Essential analyzing for researchers and practitioners in speech examine teams focused 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 easy methods to keep an eye on and make the most of Crowd?Collected Speech (pages 106–136): Ian McGraw and Joseph Polifroni
Chapter 6 an summary (pages 137–172): Martin Cooke, Jon Barker and Maria Luisa Garcia Lecumber
Chapter 7 Crowdsourced evaluation of Speech Synthesis (pages 173–216): Sabine Buchholz, Javier Latorre and Kayoko Yanagisawa
Chapter eight Crowdsourcing for Spoken conversation approach assessment (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 business Spoken conversation platforms (pages 280–302): David Suendermann and Roberto Pieraccini
Chapter eleven financial and moral historical past of Crowdsourcing for Speech (pages 303–334): Gilles Adda, Joseph J. Mariani, Laurent Besacier and Hadrien Gelas

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

Sample text

Open innovation: Use of sources outside the group to generate and implement ideas (Chaordix 2012). The reader should note that this geography is very new and thus constantly changing. Some companies may have sprouted up since this chapter was written, and some may have disappeared. The Basics 17 Most of the sites that have sprung up recently are US based. A few non-US sites have been mentioned in later chapters. com (Taskcn 2012). The globalization of crowdsourcing is essential to its success in such areas as machine translation where access to native speakers of a given language may be limited on some US platforms.

In summary, there are several actions that a requester can take to ensure better audio. 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.

Chernova et al. (2010) collected dialog data. They recorded the worker and then showed them the ASR transcription of what they had just said, using the WAMI toolkit (Gruenstein et al. 2009). Since the workers were participating in a dialog, if they saw that the system had made an error, they simply used their next dialog turn to attempt to correct it. Novotney and Callison-Burch (2010) used an MP3 upload and instructions for the use of the The Basics 19 recording software, but they did not report on whether they had checked in some way to see if the worker had complied or if they, too, relied on worker honesty.

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