Arabic Broadcast News Transcripts
|Item Name:||Arabic Broadcast News Transcripts|
|Author(s):||Mohamed Maamouri, David Graff, Christopher Cieri|
|LDC Catalog No.:||LDC2006T20|
|Release Date:||December 19, 2006|
|Data Source(s):||broadcast news|
|Application(s):||machine translation, machine learning|
LDC User Agreement for Non-Members
|Online Documentation:||LDC2006T20 Documents|
|Licensing Instructions:||Subscription & Standard Members, and Non-Members|
|Citation:||Maamouri, Mohamed, David Graff, and Christopher Cieri. Arabic Broadcast News Transcripts LDC2006T20. Web Download. Philadelphia: Linguistic Data Consortium, 2006.|
Arabic Broadcast News Transcripts was developed by the Linguistic Data Consortium (LDC) and consists of ten hours of transcribed speech from Voice of America satellite radio news broadcasts in Arabic recorded by LDC between June 2000 and January 2001. The corresponding speech files are available in Arabic Broadcast News Speech (LDC2006S46).
This work was undertaken in the Networking Data Centers (NetDC) project (MLIS-5017, NSF III-9982201) in conjunction with the European Language Resources Association (ELRA). ELRA transcribed 22.5 hours of Arabic broadcast data from Radio Orient (France) that is available in NetDC Arabic BNSC (Broadcast News Speech Corpus) (ELRA-S0157). The goal of the NetDC project was to improve the infrastructure for language resources by designing and implementing new modes of cooperation between LDC and ELRA.
The character encoding is entirely in ASCII; Buckwalter transliteration is used for rendering the Arabic text content. Time alignment and structural markup are rendered via "pseudo-SGML" tags, which are presented one tag per line, with the first character of the line being an open angle bracket.
The lines of transcription text (i.e. the speech and annotation content between the time-stamp tags) all begin with a single space character, and present exactly one token per line. (A "token" may be a spoken Arabic word, a punctuation mark, or a single Arabic word enclosed by "(%" and ")", which represents an annotation of a non-speech condition or event (e.g. "music", "noise", "laugh", etc).
For an example of the data contained in this corpus, please examine this screenshot of the transcription.