Morphological decomposition in Arabic ASR systems

Authors:
F. Diehl;M. J. F. Gales;M. Tomalin;P. C. Woodland
Affiliations:
Department of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, UK;Department of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, UK;Department of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, UK;Department of Engineering, University of Cambridge, Trumpington St., Cambridge CB2 1PZ, UK
Venue:
Computer Speech and Language
Year:
2012

Citing 3
Cited 0

N-gram-based Machine Translation

Computational Linguistics
Improved morphological decomposition for Arabic broadcast news transcription

ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Advances in Arabic Speech Transcription at IBM Under the DARPA GALE Program

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent years, the use of morphological decomposition strategies for Arabic Automatic Speech Recognition (ASR) has become increasingly popular. Systems trained on morphologically decomposed data are often used in combination with standard word-based approaches, and they have been found to yield consistent performance improvements. The present article contributes to this ongoing research endeavour by exploring the use of the 'Morphological Analysis and Disambiguation for Arabic' (MADA) tools for this purpose. System integration issues concerning language modelling and dictionary construction, as well as the estimation of pronunciation probabilities, are discussed. In particular, a novel solution for morpheme-to-word conversion is presented which makes use of an N-gram Statistical Machine Translation (SMT) approach. System performance is investigated within a multi-pass adaptation/combination framework. All the systems described in this paper are evaluated on an Arabic large vocabulary speech recognition task which includes both Broadcast News and Broadcast Conversation test data. It is shown that the use of MADA-based systems, in combination with word-based systems, can reduce the Word Error Rates by up to 8.1% relative.