Sirvi Autor "Richter, Caitlin Laura" järgi

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  • Pisipilt
    Kirje
    Assessed and Annotated Vowel Lengths in Spoken Icelandic Sentences for L1 and L2 Speakers: A Resource for Pronunciation Training
    (University of Tartu Library, 2025-03) Richter, Caitlin Laura; Friðriksdóttir, Kolbrún; Bergsson, Kormákur Logi; Maher, Erik Anders; Benediktsdóttir, Ragnheiður María; Gudnason, Jon; Johansson, Richard; Stymne, Sara
    We introduce a dataset of time-aligned phonetic transcriptions focusing on vowel length (quantity) in Icelandic. Ultimately, this aims to support computer assisted pronunciation training (CAPT) software, to automatically assess length and possible errors in Icelandic learners' pronunciations. The dataset contains a range of long and short vowel targets, including the first acoustic description of quantity in non-native Icelandic. Evaluations assess how manual annotations and automatic forced alignment characterise quantity contrasts. Initial analyses also imply partial acquisition of phonologically conditioned quantity alternations by non-native speakers.
  • Pisipilt
    Kirje
    Testing relevant linguistic features in automatic CEFR skill level classification for Icelandic
    (University of Tartu Library, 2025-03) Richter, Caitlin Laura; Ingason, Anton Karl; Glišić, Isidora; Johansson, Richard; Stymne, Sara
    This paper explores the use of various linguistic features to develop models for automatic classification of language proficiency on the CEFR scale for Icelandic, a low-resourced and morphologically complex language. We train two classifiers to assess skill level of learner texts. One is used as a baseline and takes in the original unaltered text written by a learner and uses predominantly surface features to assess the level. The other uses both surface and other morphological and lexical features, as well as context vectors from transformer (IceBERT). It takes in both the original and corrected versions of the text and takes into account errors/deviation of the original texts compared to the corrected versions. Both classifiers show promising results, with baseline models achieving between 62.2-67.1% accuracy and dual-version between 75-80.3%.