Crowdsource a little to label a lot: Labeling a speech corpus of dialectal Arabic

Samantha Wray, Ahmed Ali

Research output: Contribution to journalConference article

Abstract

Arabic is a language with great dialectal variety, with Modern Standard Arabic (MSA) being the only standardized dialect. Spoken Arabic is characterized by frequent code-switching between MSA and Dialectal Arabic (DA). DA varieties are typically differentiated by region, but despite their wide-spread usage, they are under-resourced and lack viable corpora and tools necessary for speech recognition and natural language processing. Existing DA speech corpora are limited in scope, consisting of mainly telephone conversations and scripted speech. In this paper we describe our efforts for using crowdsourcing to create a labeled multi-dialectal speech corpus. We obtained utterance-level dialect labels for 57 hours of high-quality audio from Al Jazeera consisting of four major varieties of DA: Egyptian, Levantine, Gulf, and North African. Using speaker linking to identify utterances spoken by the same speaker, and measures of label accuracy likelihood based on annotator behavior, we automatically labeled an additional 94 hours. The complete corpus contains 850 hours with approximately 18% DA speech.

Original languageEnglish (US)
Pages (from-to)2824-2828
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2015-January
StatePublished - Jan 1 2015
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany
Duration: Sep 6 2015Sep 10 2015

Fingerprint

Labeling
Labels
Speech Recognition
Speech recognition
Telephone
Natural Language
Linking
Likelihood
Necessary
Corpus
Speech
Processing
Standards
Utterance

Keywords

  • Arabic
  • Corpora creation
  • Crowdsourcing
  • Dialect classification
  • Human computation
  • Speech corpora

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

Cite this

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abstract = "Arabic is a language with great dialectal variety, with Modern Standard Arabic (MSA) being the only standardized dialect. Spoken Arabic is characterized by frequent code-switching between MSA and Dialectal Arabic (DA). DA varieties are typically differentiated by region, but despite their wide-spread usage, they are under-resourced and lack viable corpora and tools necessary for speech recognition and natural language processing. Existing DA speech corpora are limited in scope, consisting of mainly telephone conversations and scripted speech. In this paper we describe our efforts for using crowdsourcing to create a labeled multi-dialectal speech corpus. We obtained utterance-level dialect labels for 57 hours of high-quality audio from Al Jazeera consisting of four major varieties of DA: Egyptian, Levantine, Gulf, and North African. Using speaker linking to identify utterances spoken by the same speaker, and measures of label accuracy likelihood based on annotator behavior, we automatically labeled an additional 94 hours. The complete corpus contains 850 hours with approximately 18{\%} DA speech.",
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N2 - Arabic is a language with great dialectal variety, with Modern Standard Arabic (MSA) being the only standardized dialect. Spoken Arabic is characterized by frequent code-switching between MSA and Dialectal Arabic (DA). DA varieties are typically differentiated by region, but despite their wide-spread usage, they are under-resourced and lack viable corpora and tools necessary for speech recognition and natural language processing. Existing DA speech corpora are limited in scope, consisting of mainly telephone conversations and scripted speech. In this paper we describe our efforts for using crowdsourcing to create a labeled multi-dialectal speech corpus. We obtained utterance-level dialect labels for 57 hours of high-quality audio from Al Jazeera consisting of four major varieties of DA: Egyptian, Levantine, Gulf, and North African. Using speaker linking to identify utterances spoken by the same speaker, and measures of label accuracy likelihood based on annotator behavior, we automatically labeled an additional 94 hours. The complete corpus contains 850 hours with approximately 18% DA speech.

AB - Arabic is a language with great dialectal variety, with Modern Standard Arabic (MSA) being the only standardized dialect. Spoken Arabic is characterized by frequent code-switching between MSA and Dialectal Arabic (DA). DA varieties are typically differentiated by region, but despite their wide-spread usage, they are under-resourced and lack viable corpora and tools necessary for speech recognition and natural language processing. Existing DA speech corpora are limited in scope, consisting of mainly telephone conversations and scripted speech. In this paper we describe our efforts for using crowdsourcing to create a labeled multi-dialectal speech corpus. We obtained utterance-level dialect labels for 57 hours of high-quality audio from Al Jazeera consisting of four major varieties of DA: Egyptian, Levantine, Gulf, and North African. Using speaker linking to identify utterances spoken by the same speaker, and measures of label accuracy likelihood based on annotator behavior, we automatically labeled an additional 94 hours. The complete corpus contains 850 hours with approximately 18% DA speech.

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KW - Corpora creation

KW - Crowdsourcing

KW - Dialect classification

KW - Human computation

KW - Speech corpora

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