IARPA Babel Javanese Language Pack IARPA-babel402b-v1.0b
|Item Name:||IARPA Babel Javanese Language Pack IARPA-babel402b-v1.0b|
|Author(s):||Aric Bills, Judith Bishop, Thomas Conners, Anne David, Luanne Dela Cruz, Eyal Dubinski, Jonathan G. Fiscus, Ketty Gann, Mary Harper, Michael Kazi, Hanh Le, Nicolas Malyska, Jennifer Melot, Jessica Ray, Fred Richardson, Anton Rytting, Jacqui Zwanenburg|
|LDC Catalog No.:||LDC2020S07|
|Release Date:||July 15, 2020|
|DCMI Type(s):||Sound, Text|
|Data Source(s):||telephone conversations|
IARPA Babel Javanese Agreement (For-Profit)
IARPA Babel Javanese Agreement (Non-Member)
IARPA Babel Javanese Agreement (Not-For-Profit)
|Online Documentation:||LDC2020S07 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Bills, Aric, et al. IARPA Babel Javanese Language Pack IARPA-babel402b-v1.0b LDC2020S07. Web Download. Philadelphia: Linguistic Data Consortium, 2020.|
IARPA Babel Javanese Language Pack IARPA-babel402b-v1.0b was developed by Appen for the IARPA (Intelligence Advanced Research Projects Activity) Babel program. It contains approximately 204 hours of Javanese conversational and scripted telephone speech collected in 2014 and 2015 along with corresponding transcripts.
The Babel program focused on underserved languages and sought to develop speech recognition technology that could be rapidly applied to any human language to support keyword search performance over large amounts of recorded speech.
The Javanese speech in this release represents the Central, Western, and Eastern Javanese dialect regions of Indonesia. The gender distribution among speakers is approximately equal; speakers' ages range from 16 years to 65 years. Calls were made using different telephones (e.g., mobile, landline) from a variety of environments including the street, a home or office, a public place, and inside a vehicle.
Audio data is presented as 8kHz 8-bit a-law encoded audio in sphere format and 48kHz 24-bit PCM encoded audio in wav format.
Transcripts are encoded in UTF-8 in Latin script; they cover approximately 77% of the speech data. Further information about transcription methodology is contained in the documentation accompanying this release.
None at this time.