1 |
Conventional Orthography for Dialectal Arabic (CODA): Principles and Guidelines -- Egyptian Arabic - Version 0.7 - March 2012
|
|
|
|
BASE
|
|
Show details
|
|
2 |
Conventional Orthography for Dialectal Arabic (CODA): Principles and Guidelines -- Egyptian Arabic - Version 0.7 - March 2012 ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
Annotation Guidelines for Arabic Nominal Gender, Number, and Rationality
|
|
|
|
BASE
|
|
Show details
|
|
4 |
LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual
|
|
|
|
BASE
|
|
Show details
|
|
5 |
Annotation Guidelines for Arabic Nominal Gender, Number, and Rationality ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Improved arabic-to-english statistical machine translation by reordering post-verbal subjects for word alignment
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Conventional Orthography for Dialectal Arabic (CODA) Version 0.1 ““ July 2011
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Conventional Orthography for Dialectal Arabic (CODA) Version 0.1 ““ July 2011 ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Dialectal to Standard Arabic Paraphrasing to Improve Arabic-English Statistical Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Automatic Identification of Errors in Arabic Handwriting Recognition
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Automatic Identification of Errors in Arabic Handwriting Recognition ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Spoken Arabic Dialect Identification Using Phonotactic Modeling
|
|
|
|
Abstract:
The Arabic language is a collection of multiple variants, among which Modern Standard Arabic (MSA) has a special status as the formal written standard language of the media, culture and education across the Arab world. The other variants are informal spoken dialects that are the media of communication for daily life. Arabic dialects differ substantially from MSA and each other in terms of phonology, morphology, lexical choice and syntax. In this paper, we describe a system that automatically identifies the Arabic dialect (Gulf, Iraqi, Levantine, Egyptian and MSA) of a speaker given a sample of his/her speech. The phonotactic approach we use proves to be effective in identifying these dialects with considerable overall accuracy — 81.60% using 30s test utterances.
|
|
Keyword:
Computer science
|
|
URL: https://doi.org/10.7916/D8NC68H1
|
|
BASE
|
|
Hide details
|
|
19 |
Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-Based Pronunciation Rules
|
|
|
|
BASE
|
|
Show details
|
|
|
|