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Objective: This study investigated the degrees of lenition, or consonantal weakening, in the production of Spanish stop consonants by native English speakers during a study abroad (SA) program. Lenition is a key phonological process in Spanish, where voiced stops (/b/, /d/, /ɡ/) typically weaken to fricatives or approximants in specific phonetic environments. For L2 learners, mastering this subtle process is essential for achieving native-like pronunciation. Methods: To assess the learners’ progress in acquiring lenition, we employed Phonet, a deep learning model. Unlike traditional quantitative acoustic methods that focus on measuring the physical properties of speech sounds, Phonet utilizes recurrent neural networks to predict the posterior probabilities of phonological features, particularly sonorant and continuant characteristics, which are central to the lenition process. Results: The results indicated that while learners showed progress in producing the fricative-like variants of lenition during the SA program and understood how to produce lenition in appropriate contexts, the retention of these phonological gains was not sustained after their return. Additionally, unlike native speakers, the learners never fully achieved the approximant-like realization of lenition. Conclusions: These findings underscore the need for sustained exposure and practice beyond the SA experience to ensure the long-term retention of L2 phonological patterns. While SA programs offer valuable opportunities for enhancing L2 pronunciation, they should be supplemented with ongoing support to consolidate and extend the gains achieved during the immersive experience.
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This content will become publicly available on January 1, 2026
Measuring the Impact of Segmental Deviation on Perceptions of Accentedness using Gradient Phonological Class Features
Using Phonet (Vásquez-Correa et al., 2019), a neural network-based model, we generate vector representations of speech segments consisting of phonological class probabilities and use these representations to quantify segmental deviations in the English of native Hindi speakers from American English (AE) and Indian English (IE) baselines, in order to explain how these deviations impact perceptions of accentedness by native AE speakers. The primary focus is on three AE phonemes and their realizations in Hindi English (HE) and Indian English: the labiovelar approximant /w/, often produced as the labiodental approximant [ʋ]; the alveolar stop /t/, commonly realized as the retroflex stop [ʈ]; and the rhotic approximant /ɹ/,rendered as the rhotic tap [ɾ]. Multinomial logistic regressions of Euclidean distances from HE sements to AE/IE baselines on accent ratings show that larger distances from AE baselines increase the likelihood of perceiving stronger accents while larger distances from IE baselines decrease the likelihood. Changes in the probability distributions of contrastive phonological classes are found to correlate with the strength of the perceived accent. These results offer valuable insights into the interplay between native phonology and the perception of accented speech.
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- Award ID(s):
- 2037266
- PAR ID:
- 10639426
- Publisher / Repository:
- University of Massachusetts Amherst Libraries
- Date Published:
- Journal Name:
- Society for Computation in Linguistics
- Volume:
- 8
- Issue:
- 1
- ISSN:
- 2834-1007
- Subject(s) / Keyword(s):
- SLM SLM-r PAM Neural Networks accentedness phonology
- Format(s):
- Medium: X Other: application/pdf
- Sponsoring Org:
- National Science Foundation
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