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Publication . Conference object . Article . Preprint . Contribution for newspaper or weekly magazine . 2016

Automatic Dialect Detection in Arabic Broadcast Speech

Ahmed Ali; Najim Dehak; Patrick Cardinal; Sameer Khurana; Sree Harsha Yella; James Glass; Peter Bell; +1 Authors
Open Access
Abstract

In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We also evaluated these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further reported results using the proposed methods to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 59.2%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. All the data used on our experiments have been released to the public as a language identification corpus.

Subjects by Vocabulary

Microsoft Academic Graph classification: Context (language use) Classifier (linguistics) Artificial intelligence business.industry business Natural language processing computer.software_genre computer Support vector machine Binary classification Identification (information) Test data Generative grammar Modern Standard Arabic language.human_language language Computer science

ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION

Subjects

Computer Science - Computation and Language, Computation and Language (cs.CL), FOS: Computer and information sciences

20 references, page 1 of 2

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Funded by
EC| SUMMA
Project
SUMMA
Scalable Understanding of Multilingual Media
  • Funder: European Commission (EC)
  • Project Code: 688139
  • Funding stream: H2020 | RIA
Validated by funder
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