Apps for developing pronunciation in English as an L2
Bibliographic Data
| ID | 21848992 |
|---|---|
| Authors | Luana Garbin Baldissera (0000-0001-9027-8951, Universidade Federal de Santa Catarina), Celso Henrique Soufen Tumolo (0000-0001-5045-8712, Universidade Federal de Santa Catarina) |
| Year | 2021 |
| Volume | 16 |
| Issue | 5 |
| Pages | 1355 |
| Publication date | 2021-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Revista X (JOURNAL) |
| Journal identifiers | ISSN: 1980-0614 • E-ISSN: 1980-0614 |
| Publisher | Universidade Federal do Parana (PUBLISHER • BR) |
| DOI | 10.5380/rvx.v16i5.81057 |
| OpenAlex | W3196652305 |
| Language | EN |
| Citations received | 1 |
| References cited | 4 |
The goal of pronunciation teaching should be to enable learners to develop intelligible pronunciation and, in order to do this, it is important to teach perception and production of the most relevant segmental and suprasegmental features of pronunciation, considering specific groups of learners (CELCE-MURCIA et al. , 2010). Technology has played an important role in pronunciation teaching, and the applications developed for pronunciation instruction enable learners not only to engage in pronunciation activities, but to have access to a greater variety of input and immediate feedback. Having this in mind, this study aimed at analyzing the content, the pronunciation teaching steps, the features, and usability resources of pronunciation apps. In order to guide the analysis, a framework was developed based on literature related to the areas of pronunciation teaching and of Mobile Assisted Language Learning (MALL). The results showed that there is a tendency for the apps analyzed to focus more on segmentals. All of them offer description and analysis, listening discrimination, and controlled practice of the pronunciation features, as well as feedback. However, they were limited in terms of guided and communicative practice, of Automated Speech Recognition (ASR), and of variety of input
Active listening · Human–computer interaction · Linguistics · Natural language processing · Perception · Pronunciation · Usability · Communication · Computer Science · Mobile Learning in Education · Phonetics and Phonology Research · Psychology · Speech and dialogue systems · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |