Work

Identifying dialects with textual and acoustic cues

Year: 2017

Type: conference-paper

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.

Source: Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial)

DOI: 10.18653/v1/w17-1211

Authors: Abualsoud Hanani, Aziz Qaroush, Stephen Taylor

Institutions: Birzeit University, Fitchburg State University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W2741270771

FWCI: 1.497

Cites: 27

Cited by: 16

Related to: 10