Development of the Arabic Loria Automatic Speech Recognition system (ALASR) and its evaluation for Algerian dialect

International audienceThis paper addresses the development of an Automatic Speech Recognition system for Modern Standard Arabic (MSA) and its extension to Algerian dialect. Algerian dialect is very different from Arabic dialects of the Middle-East, since it is highly influenced by the French language. In this article, we start by presenting the new automatic speech recognition named ALASR (Arabic Loria Automatic Speech Recognition) system. The acoustic model of ALASR is based on a DNN approach and the language model is a classical n-gram. Several options are investigated in this paper to find the best combination of models and parameters. ALASR achieves good results for MSA in terms of WER (14.02%), but it completely collapses on an Algerian dialect data set of 70 minutes (a WER of 89%). In order to take into account the impact of the French language, on the Algerian dialect, we combine in ALASR two acoustic models, the original one (MSA) and a French one trained on ESTER corpus. This solution has been adopted because no transcribed speech data for Algerian dialect are available. This combination leads to a substantial absolute reduction of the word error of 24%. c 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd International Conference on Arabic Computational Linguistics

Improved morphological decomposition for Arabic broadcast news transcription

In this paper, we show the progress for Arabic speech recognition by incorporating contextual information into the process of morphological decomposition. The new approach achieves lower out-of-vocabulary and word error rates when compared to our previous work, in which the morphological decomposition relies on word-level information only. We also describe how the vocalization procedure is improved to produce pronunciations for some dialect Arabic words. By using the new approach, we reduced the word error by 0.8% absolute (4.7% relative) when compared to the baseline approach.United States. Defense Advanced Research Projects Agency (DARPA). GALE program (Contract No. HR0011-06-C-0022