Assessed and Annotated Vowel Lengths in Spoken Icelandic Sentences for L1 and L2 Speakers: A Resource for Pronunciation Training (2025)

Abstract: 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.

## introduction we present a corpus of icelandic speech with manually corrected time-aligned phonetic transcriptions, targeted towards native and non-native icelandic speakers' acoustic realisations of vowel quantity (length). quantity is important in non-native (l2) icelandic learning because it is contrastive, as in vinur [vi:nyr] 'friend', vinnur [vin:yr] 'you, s/he work(s)', but challenging for many learners whose first languages do not use this cue. computer assisted language learning (call) such as pronunciation training (capt) enables self-directed learning beyond traditional classrooms, and could provide opportunities to practice and internalise the icelandic quantity system. the acoustic implementation of icelandic quantity has been studied only in small manually annotated native-speaker (l1) datasets. addressing learners' needs requires (i) understanding quantity realisation in a broad sample of l1 and l2 speech, and (ii) developing scalable automated methods to describe a sufficient sample of the language and to evaluate learners' speech relative to acoustic targets in autonomous interactive capt software. we release time-aligned phonetic annotations for 2707 tokens of 72 icelandic words, foot_0 greatly increasing the variety of contexts with available acoustic data on quantity, and including non-native speech for the first time. §4 uses this data to explore the realisation of quantity contrasts, comparing manual annotations and automated equivalents from the montreal forced aligner (mfa), to address four research questions: rq1 how do (subsets of) the annotated data relate to expectations from comparable studies? rq2 how strongly do quantity contrasts emerge in the annotated features, for l1 and l2 speakers? rq3 how accurate is montreal forced aligner (mfa) timing, compared to gold annotations? rq4 how useful is mfa for issues in rqs 1-2? ## discussion at a high level, rq1 is answered positively, as the conventional ratio proves to be an informative and interpretable feature, and more useful than absolute vowel duration alone. more specifically, for -ala, -alla words, expectations from a controlled study were strongly upheld in our crowdsourced data. for -aki, -aggi, aggregated data also would seem to match expectations, but a substantial proportion of individual tokens occupy an ambiguous region, at least in all currently examined feature spaces. regarding rq2, quantity can be classified from the ratio feature, but long and short vowels are not always well separable, and absolute durations of vowel and consonant carry some useful information beyond the ratio. location of a best threshold for any features also varies based on several other factors. in some cases, factors are identified and controlled for, with good to excellent classifier performance. in other cases this work is ongoing, and a general-purpose solution remains to be developed; it could require phoneme identity labels, representations of syllable and word structure, prosodic environment, spectral features, etc. qualitatively, during annotation we had observed noticeable length errors in some of the same l2 samples (e.g. tvisvar, hausnum) where the measured features indicated loss of contrast for l2 speakers as compared to l1, which is an encouraging sign that the features can capture perceptually important dimensions of contrast. addressing rq3, mfa frequently mismeasures segments in this corpus by around one-third of the true duration, although the particular values for all mfa measures arise from a specific acoustic model and do not generalise to others. the relevant interpretation is that typical applications of mfa, like ours obtaining decent word alignments from 20 hours of in-domain training speech, cannot be relied on for the accuracy desired by primary de-scriptive research in phonetic segments. mfa preprocessing may also introduce bias in the gold annotations, which would not critically affect capt development and is well worth the saved time over full manual transcription, but true inaccuracy of mfa may be underestimated. despite considerable room for improvement, alignment errors had small impacts (rq4) on classifiers' ability to distinguish short and long vowels. the relative utility of various features also appears similar whether using manual or automated data.