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
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Improving the Arabic Pronunciation Dictionary for Phone and Word Recognition with Linguistically-Based Pronunciation Rules
|
|
|
|
Abstract:
In this paper, we show that linguistically motivated pronunciation rules can improve phone and word recognition results for Modern Standard Arabic (MSA). Using these rules and the MADA morphological analysis and disambiguation tool, multiple pronunciations per word are automatically generated to build two pronunciation dictionaries; one for training and another for decoding. We demonstrate that the use of these rules can significantly improve both MSA phone recognition and MSA word recognition accuracies over a baseline system using pronunciation rules typically employed in previous work on MSA Automatic Speech Recognition (ASR). We obtain a significant improvement in absolute accuracy in phone recognition of 3.77%–7.29% and a significant improvement of 4.1% in absolute accuracy in ASR.
|
|
Keyword:
Computer science
|
|
URL: https://doi.org/10.7916/D81N88F2
|
|
BASE
|
|
Hide details
|
|
|
|