Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
To rapidly port speech applications to new languages one of the most difficult tasks is the initial collection of sufficient speech corpora. State-of-the-art automatic speech recognition systems are typical trained on hundreds of hours of speech data. While pre-existing corpora do exist for major languages, a sufficient amount of quality speech data is not available for most world languages. While previous works have focused on the collection of translations and the transcription of audio via Mechanical-Turk mechanisms, in this paper we introduce two tools which enable the collection of speech data remotely. We then compare the quality of audio collected from paid part-time staff and unsupervised volunteers, and determine that
basic user training is critical to obtain usable data.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Barlas Oğuz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Wen-tau Yih