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Conference Papers Year : 2022

Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation

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Abstract

Speech characteristics vary from speaker to speaker. While some variation phenomena are due to the overall communication setting, others are due to diastratic factors such as gender, provenance, age, and social background. The analysis of these factors, although relevant for both linguistic and speech technology communities, is hampered by the need to annotate existing corpora or to recruit, categorise, and record volunteers as a function of targeted profiles. This paper presents a methodology that uses a knowledge base to provide speaker-specific information. This can facilitate the enrichment of existing corpora with new annotations extracted from the knowledge base. The method also helps the large scale analysis by automatically extracting instances of speech variation to correlate with diastratic features. We apply our method to an over 120-hour corpus of broadcast speech in French and investigate variation patterns linked to reduction phenomena and/or specific to connected speech such as disfluencies. We find significant differences in speech rate, the use of filler words, and the rate of non-canonical realisations of frequent segments as a function of different professional categories and age groups.
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Dates and versions

hal-03860760 , version 1 (18-11-2022)

Identifiers

  • HAL Id : hal-03860760 , version 1

Cite

Yaru Wu, Fabian Suchanek, Ioana Vasilescu, Lori Lamel, Martine Adda-Decker. Using a Knowledge Base to Automatically Annotate Speech Corpora and to Identify Sociolinguistic Variation. 13th Conference on Language Resources and Evaluation (LREC 2022), Jun 2022, Marseille, France. pp.1054-1360. ⟨hal-03860760⟩
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