Archives: Events
Andrea Farina (King’s College) – “Going away in Ancient Greek and Latin. A corpus-based quantitative approach to Source preverbs and motion verbs”
Hilke Ceuppens (KU Leuven) – “Semantic loss in the lexicon of English: the role of contextual differentiation” (joint work with Hendrik De Smet)
Jóhanna Barðdal (UGent) – “Dependent-Marked Anticausatives in Old Norse-Icelandic: The Case of the Accusative Case”
Giovanni Leo (UGent) – “Intonation in Language Contact: the Case of Simultaneous Italian-Dialect Bilingualism”
Katharine Shields (King’s College) – “Historical linguistics and early modern dictionaries of Ancient Greek”
Liliane Haegeman (UGent) – “Styling the character: subject drop in Agatha Christie” (joint work with Lieven Danckaert)
Leonid Kulikov (UGent): “Noun incorporation as a marginal transitivity-related phenomenon in Indo-European (and beyond), or How to distinguish between noun incorporation and nominal composition (a diachronic typological perspective)”
Giuseppe Magistro (UGent) – “Creating a corpus of web-data with Pyrlato. A demonstration”
The use of corpora in acoustic analyses has become a standard practice in phonetic phonological research, offering high ecological validity (see e.g. Beckman, 1997; Warner, 2012; Tucker & Mukai, 2023 for a discussion on validity). However, compiling corpora and looking for specific phenomena can be time and resource-consuming. In response to this challenge, we developed a program named Pyrlato, which we aim to demonstrate. Pyrlato is a novel tool designed for creating corpora of real-world spoken data from the web. The tool extracts audio files from YouTube, cutting and extracting desired segments such as specific phonemes, syllables, or words found in YouTube videos. This enables the creation of corpora with tens of thousands of tokens within a few computational hours. Pyrlato works across Dutch, English, French, German, Indonesian, Italian, Japanese, Korean, Portuguese, Russian, Spanish, Turkish, Ukrainian, and Vietnamese, i.e. those languages for which YouTube provides automatic subtitles. The software searches for the desired string in the subtitles and, upon finding the match, extracts the relevant audio extract containing the string in .mp3 format (other formats are also possible).
The demonstration will showcase Pyrlato’s online version and the application of some case studies.
• Beckman, M.E. (1997).A typology of spontaneous speech. In Y. Sagisaka, N. Campbell, & N. Higuchi (Eds.), Computing Prosody: Computational Models for Processing Spontaneous Speech (pp. 7–26). Springer. http://dx.doi.org/10.1007/978-1-4612-2258-3_2.
• Tucker, B.V., & Mukai, Y. (2023). Spontaneous speech. Cambridge University Press. http://doi.org/10.1017/9781108943024.
• Warner, N. (2012). Methods for studying spontaneous speech. In A. Cohn, C. Fougeron, & M. Huffman (Eds.), The Oxford Handbook of Laboratory Phonology (pp. 621–633). Oxford University Press.