Work

Improving the Arabic pronunciation dictionary for phone and word recognition with linguistically-based pronunciation rules

Year: 2009

Type: conference-paper

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.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.3115/1620754.1620812

Authors: Fadi Biadsy, Nizar Habash, Julia Hirschberg

Institution: Columbia University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Defense Advanced Research Projects Agency, Advanced Research Projects Agency

Award: G3190074090