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Abstract / Description of output
This paper describes the Arabic Multi-Genre Broadcast (MGB-2) Challenge for SLT-2016. Unlike last year’s English MGB Challenge, which focused on recognition of diverse TV genres, this year, the challenge has an emphasis on handling the diversity in dialect in Arabic speech. Audio data comes from 19 distinct programmes from the Aljazeera Arabic TV channel between March 2005 and December 2015. Programmes are split into three groups: conversations, interviews, and reports. A total of 1,200 hours have been released with lightly supervised transcriptions for the acoustic modelling. For language modelling, we made available over 110M words crawled from Aljazeera Arabic website Aljazeera.net for a 10 year duration 2000-2011. Two lexicons have been provided, one phoneme based and one grapheme based. Finally, two tasks were proposed for this year’s challenge: standard speech transcription, and word alignment. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.
Original language | English |
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Title of host publication | 2016 IEEE Workshop on Spoken Language Technology |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 279-284 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-5090-4903-5 |
DOIs | |
Publication status | Published - 9 Feb 2017 |
Event | 2016 IEEE Workshop on Spoken Language Technology - San Diego, United States Duration: 13 Dec 2016 → 16 Dec 2016 https://www2.securecms.com/SLT2016//Default.asp |
Conference
Conference | 2016 IEEE Workshop on Spoken Language Technology |
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Abbreviated title | SLT 2016 |
Country/Territory | United States |
City | San Diego |
Period | 13/12/16 → 16/12/16 |
Internet address |
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Dive into the research topics of 'The MGB-2 Challenge: Arabic Multi-Device Broadcast Media Recognition'. Together they form a unique fingerprint.Projects
- 1 Finished
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SUMMA - Scalable Understanding of Mulitingual Media
Renals, S., Birch-Mayne, A. & Cohen, S.
1/02/16 → 31/01/19
Project: Research