JOURNAL ARTICLE
Identifying dialects with textual and acoustic cues
Abstract
We describe several systems for identifying short samples of Arabic or Swiss-German dialects, which were prepared for the shared task of the 2017 DSL Workshop (Zampieri et al., 2017). The Arabic data comprises both text and acoustic files, and our best run combined both. The Swiss-German data is text-only. Coincidently, our best runs achieved a accuracy of nearly 63% on both the Swiss-German and Arabic dialects tasks.
Keywords:
German Arabic Computer science Task (project management) Natural language processing Digital subscriber line Artificial intelligence Speech recognition Linguistics Engineering Telecommunications
Metrics
17
Cited By
2.98
FWCI (Field Weighted Citation Impact)
27
Refs
0.92
Citation Normalized Percentile
Is in top 1%
Is in top 10%
Citation History
Topics
Natural Language Processing Techniques
Physical Sciences → Computer Science → Artificial Intelligence
Authorship Attribution and Profiling
Physical Sciences → Computer Science → Artificial Intelligence
Speech Recognition and Synthesis
Physical Sciences → Computer Science → Artificial Intelligence
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