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Pluricentric languages are languages that are spoken in at least two countries where they have an official function and thus develop national varieties with specific linguistic and pragmatic features. Presently 43 languages have been identified as belonging to this category, for instance, English, Spanish, German, Bengali, Hindi and Urdu. This article forms an introduction to the special issue "Pluricentric Languages in Speech Science and Technology" by giving an overview of current challenges with respect to the development of speech and language resources, annotation and analysis tools, as well as speech technology services for pluricentric languages. The article discusses potential solutions that come from cross-fertilization: on the one hand, how phonetic and linguistic knowledge may contribute to advancements in speech technology, and on the other, how speech technology may facilitate phonetic and linguistic studies on pluricentric languages. In our discussion, we include the research methods and findings of the eight research articles of this special issue and point towards promising paths for future research in the field. [ABSTRACT FROM AUTHOR]

Acoustic metrics of contrastive speech rhythm, based on vocalic and intervocalic interval durations, are intended to capture stable typological differences between languages. They should consequently be robust to variation between speakers, sentence materials, and measurers. This paper assesses the impact of these sources of variation on the metrics %V (proportion of utterance comprised of vocalic intervals), VarcoV (rate-normalized standard deviation of vocalic interval duration), and nPVI-V (a measure of the durational variability between successive pairs of vocalic intervals). Five measurers analyzed the same corpus of speech: five sentences read by six speakers of Standard Southern British English. Differences between sentences were responsible for the greatest variation in rhythm scores. Inter-speaker differences were also a source of significant variability. However, there was relatively little variation due to segmentation differences between measurers following an agreed protocol. An automated phone alignment process was also used: Rhythm scores thus derived showed good agreement with the human measurers. A number of recommendations for researchers wishing to exploit contrastive rhythm metrics are offered in conclusion. [ABSTRACT FROM AUTHOR]