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 way of quickly processing speech data

Intended if you are looking to start within the area and  methods to organize a job, what interfaces can be found, how you can check the paintings, and so on. in addition to if you have already got used crowdsourcing and wish to create greater projects and procure higher exams of the paintings of the group. it is going to contain screenshots to teach examples of excellent and terrible interfaces; examples of case experiences in speech processing projects, facing the duty construction procedure, reviewing techniques within the interface, within the number of medium (MTurk or different) and explaining offerings, etc.

  • Provides an insightful and functional advent to crowdsourcing as a method of quickly processing speech data.
  • Addresses very important elements of this new process that are supposed to be mastered ahead of trying a crowdsourcing application.
  • Offers speech researchers the desire 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 winning 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 guidance on tips on how to play and list speech over the net, how you can layout projects, and the way to evaluate workers.

Essential interpreting for researchers and practitioners in speech study teams interested by speech processing

Chapter 1 an summary (pages 1–7): Maxine Eskenazi
Chapter 2 the fundamentals (pages 8–36): Maxine Eskenazi
Chapter three gathering Speech from Crowds (pages 37–71): Ian McGraw
Chapter four Crowdsourcing for Speech Transcription (pages 72–105): Gabriel Parent
Chapter five find out how to keep an eye on 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 evaluate 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 systems (pages 241–279): Christoph Draxler
Chapter 10 Crowdsourcing for commercial Spoken conversation platforms (pages 280–302): David Suendermann and Roberto Pieraccini
Chapter eleven fiscal and moral history of Crowdsourcing for Speech (pages 303–334): Gilles Adda, Joseph J. Mariani, Laurent Besacier and Hadrien Gelas

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

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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. Proceedings of IEEE ICASSP2012. Dredze M, Jansen A, Coppersmith G and Church K (2010) NLP on spoken documents without ASR in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing.

Audhkhasi K, Georgiou P and Narayanan S (2011b) Reliability-weighted acoustic model adaptation using crowd sourced transcriptions. Proceedings of Interspeech 2011. Bucholz S and Latorre J (2011) Crowdsourcing preference tests and how to detect cheating. Proceedings of Interspeech 2011. Callison-Burch C and Dredze M (2010) Creating speech and language data with Amazon’s Mechanical Turk. Proceedings of NAACL-2010 Workshop on Creating Speech and Language Data With Amazon’s Mechanical Turk. 34 Crowdsourcing for Speech Processing The Official CAPTCHA Site.

Some causes of audio problems are: • • • • • Worker not wearing the headset. Headset not plugged in. Sound levels too high or too low. High levels of ambient noise. Failure to correctly follow instructions. Some have argued that poor-quality audio can be dealt with. Novotney and Callison-Burch (2010) found that in the case of very large datasets, poor utterances may only comprise a small part of the whole and do not lower quality significantly for an ASR system trained on this data. However, their expression “good enough” is relevant for applications where a slightly higher word error rate (WER) is acceptable.

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