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Reducing out-of-vocabulary in morphology to improve the accuracy in Arabic dialects speech recognition

Almeman, Khalid Abdulrahman (2015)
Ph.D. thesis, University of Birmingham.

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Abstract

This thesis has two aims: developing resources for Arabic dialects and improving the speech recognition of Arabic dialects. Two important components are considered: Pronunciation Dictionary (PD) and Language Model (LM). Six parts are involved, which relate to building and evaluating dialects resources and improving the performance of systems for the speech recognition of dialects.

Three resources are built and evaluated: one tool and two corpora. The methodology that was used for building the multi-dialect morphology analyser involves the proposal and evaluation of linguistic and statistic bases. We obtained an overall accuracy of 94%. The dialect text corpora have four sub-dialects, with more than 50 million tokens. The multi-dialect speech corpora have 32 speech hours, which were collected from 52 participants. The resultant speech corpora have more than 67,000 speech files.

The main objective is improvement in the PDs and LMs of Arabic dialects. The use of incremental methodology made it possible to check orthography and phonology rules incrementally. We were able to distinguish the rules that positively affected the PDs. The Word Error Rate (WER) improved by an accuracy of 5.3% in MSA and 5% in Levantine.

Three levels of morphemes were used to improve the LMs of dialects: stem, prefix+stem and stem+suffix. We checked the three forms using two different types of LMs. Eighteen experiments are carried out on MSA, Gulf dialect and Egyptian dialect, all of which yielded positive results, showing that WERs were reduced by 0.5% to 6.8%.

Type of Work:Ph.D. thesis.
Supervisor(s):Lee, Mark
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Subjects:P Philology. Linguistics
PJ Semitic
QA75 Electronic computers. Computer science
Institution:University of Birmingham
ID Code:5763
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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