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Investigating Data Sharing in Speech Recognition for an Under-Resourced Language: The Case of Algerian Dialect

Domain:

natural language processing

Record type:

paper
Creator:
MohKam
Publisher:
AIR
Host:
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.

Visit

doi.org

Tasks

automatic speech recognitionspeech processingtransfer learning

Languages

Arabic, Algerian Spoken

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