Author : Mohamed Amine Menacer
Affiliation : Université de Lorraine
Country : France
Category : Computer Science & Information Technology
Volume, Issue, Month, Year : 11, 03, March, 2021
The Arabic language has many varieties, including its standard form, Modern Standard Arabic (MSA), and its spoken forms, namely the dialects. Those dialects are representative examples of under-resourced languages for which automatic speech recognition is considered as an unresolved issue. To address this issue, we recorded several hours of spoken Algerian dialect and used them to train a baseline model. This model was boosted afterwards by taking advantage of other languages that impact this dialect by integrating their data in one large corpus and by investigating three approaches: multilingual training, multitask learning and transfer learning. The best performance was achieved using a limited and balanced amount of acoustic data from each additional language, as compared to the data size of the studied dialect. This approach led to an improvement of 3.8% in terms of word error rate in comparison to the baseline system trained only on the dialect data.
Keyword : Automatic speech recognition, Algerian dialect, MSA, multilingual training, multitask learning, transfer learning.
For More Details : https://aircconline.com/csit/papers/vol11/csit110308.pdf
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