IARPA Babel Igbo Language Pack IARPA-babel306b-v2.0c
|Item Name:||IARPA Babel Igbo Language Pack IARPA-babel306b-v2.0c|
|Author(s):||Nikki Adams, Aric Bills, Thomas Conners, Anne David, Eyal Dubinski, Jonathan G. Fiscus, Ketty Gann, Mary Harper, Alice Kaiser-Schatzlein, Michael Kazi, Nicolas Malyska, Jennifer Melot, Akiko Onaka, Shelley Paget, Jessica Ray, Fred Richardson, Anton Rytting, Sinney Shen|
|LDC Catalog No.:||LDC2019S16|
|Release Date:||August 15, 2019|
|DCMI Type(s):||Sound, Text|
|Data Source(s):||telephone conversations|
IARPA Babel Igbo Agreement (For-Profit)
IARPA Babel Igbo Agreement (Non-Member)
IARPA Babel Igbo Agreement (Not-For-Profit)
|Online Documentation:||LDC2019S16 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Adams, Nikki, et al. IARPA Babel Igbo Language Pack IARPA-babel306b-v2.0c LDC2019S16. Web Download. Philadelphia: Linguistic Data Consortium, 2019.|
IARPA Babel Igbo Language Pack IARPA-babel306b-v2.0c was developed by Appen for the IARPA (Intelligence Advanced Research Projects Activity) Babel program. It contains approximately 207 hours of Igbo conversational and scripted telephone speech collected in 2014 and 2015 along with corresponding transcripts.
The Babel program focuses on underserved languages and seeks to develop speech recognition technology that can be rapidly applied to any human language to support keyword search performance over large amounts of recorded speech.
The Igbo speech in this release represents the Owerri, Onitsha, and Ngwa dialects spoken in Nigeria. The gender distribution among speakers is approximately equal; speakers' ages range from 16 years to 67 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 and included for approximately 65% of the speech. Further information about transcription methodology is contained in the documentation accompanying this release.
None at this time.