Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6902050 | Procedia Computer Science | 2017 | 8 Pages |
Abstract
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%.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Mohamed Amine Menacer, Odile Mella, Dominique Fohr, Denis Jouvet, David Langlois, Kamel Smaïli,