Use this URL to cite or link to this record in EThOS:
Title: Using automatic speech recognition to evaluate Arabic to English transliteration
Author: Khalil, G.
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2013
Availability of Full Text:
Access from EThOS:
Access from Institution:
Increased travel and international communication has led to an increased need for transliteration of Arabic proper names for people, places, technical terms and organisations. There are a variety of available Arabic to English transliteration systems such as Unicode, the Buckwalter Arabic transliteration, and ArabTeX. The transliteration tables have been developed and used by researchers for many years, but there are only limited attempts to evaluate and compare different transliteration systems. This thesis investigates whether or not speech recognition technology could be used to evaluate different Arabic-English transliteration systems. In order to do so there were 5 main objectives: firstly, to investigate the possibility of using English speech recognition engines to recognize Arabic words; secondly, to establish the possibility of automatic transliteration of diacritised Arabic words for the purpose of creating a vocabulary for the speech recognition engine; thirdly, to explore the possibility of automatically generating transliterations of non diacritised Arabic words; fourthly to construct a general method to compare and evaluate different transliteration; and finally, to test the system and use it to experiment with new transliterations ideas. A novel testing method was found to evaluate transliteration rules and an automatic application system has been developed. This method was used to compare five existing transliteration tables: UN, Qalam, Buckwalter, ArabTeX and Alghamdi tables. From the results of these comparisons, new rules were developed in order to improve transliteration performance; these rules achieved of score 37.9% transliteration performance which is higher than the 19.1% score achieved using Alghamdi’s table which was the best performing of the existing transliteration tables tested. Most of the improvement was obtained by changing letter(s) for letter(s) transliterations, further improvements were made by more sophisticated rules based on combinations of letters and diacritics. Speech recognition performance is not a direct test of transliteration acceptability, but does correlate well with human judgement, and offers consistency and repeatability. The issues surrounding the user of English ASR for this application are discussed, as are proposals to further improve transliteration systems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available