Analyzing second language proficiency using wavelet-based prominence estimates
Bibliographic Data
| ID | 4579459 |
|---|---|
| Authors | Heini Kallio (0000-0002-1263-9624, University of Helsinki, corresponding author), Antti Suni (0000-0003-3414-6035, University of Helsinki), Juraj Šimko (0000-0002-8693-9646, University of Helsinki), Martti Vainio (0000-0003-2570-0196, University of Helsinki) |
| Year | 2020 |
| Volume | 80 |
| Pages | 100966 |
| Publication date | 2020-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Phonetics (JOURNAL) |
| Journal identifiers | ISSN: 0095-4470 • E-ISSN: 1095-8576 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.wocn.2020.100966 |
| OpenAlex | W2991423577 |
| Language | EN |
| Citations received | 2 |
| References cited | 36 |
Prosodic characteristics, such as lexical and phrasal stress, are one of the most challenging features for second language (L2) speakers to learn. The ability to quantify language learners’ proficiency in terms of prosody can be of use to language teachers and improve the assessment of L2 speaking skills. Automatic assessment, however, requires reliable automatic analyses of prosodic features that allow for the comparison between the productions of L2 speech and reference samples. In this paper we investigate whether signal-based syllable prominence can be used to predict the prosodic competence of Finnish learners of Swedish. Syllable-level prominence was estimated for 180 L2 and 45 native (L1) utterances by a continuous wavelet transform analysis using combinations of f0, energy, and duration. The L2 utterances were graded by four expert assessors using the revised CEFR scale for prosodic features. Correlations of prominence estimates for L2 utterances with estimates for L1 utterances and linguistic stress patterns were used as a measure of prosodic proficiency of the L2 speakers. The results show that the level of agreement conceptualized in this way correlates significantly with the assessments of expert raters, providing strong support for the use of the wavelet-based prominence estimation techniques in computer-assisted assessment of L2 speaking skills
Competence (human resources) · Language proficiency · Linguistics · Mathematics education · Natural language processing · Prosody · Speech recognition · Stress (linguistics) · Syllable · Artificial Intelligence · Computer Science · Phonetics and Phonology Research · Psychology · Speech and dialogue systems
The Mutual Intelligibility of L2 Speech
Second language fluency
Learning Second Language Suprasegmentals
Regression Models for Ordinal Data
What makes speech sound fluent? The contributions of pauses, speed and repairs
How do utterance measures predict raters’ perceptions of fluency in French as a second language
Native speakers’ perceptions of fluency and accent in L2 speech
Exploring measures and perceptions of fluency in the speech of second language learners
English pronunciation and fluency development in Mandarin and Slavic speakers
Intonation, Perception and Language
Intonation Systems
Primary Stress and Intelligibility
Intelligibility and the Listener
On durational correlates of word stress in Finnish
Durational correlates of stress in Swedish, French and English
Tonal features, intensity, and word order in the perception of prominence
The Relationship Between Native Speaker Judgments of Nonnative Pronunciation and Deviance in Segmentais, Prosody, and Syllable Structure
Foreign Accent, Comprehensibility, and Intelligibility in the Speech of Second Language Learners
Second Language Fluency
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,4 |
| Citation span | 2021 - 2022 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |