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This study investigates the gradient phonetic variations in the lenition of Spanish voiced and voiceless stops among second language (L2) learners with different levels of proficiency (beginning, intermediate, and advanced). The degree of lenition is measured using posterior probabilities of the continuant and sonorant phonological features, estimated by the deep learning model Phonet. The findings reveal that the degree of lenition, as indicated by the sonorant posterior probability, increases with proficiency. However, no significant effects of proficiency were observed for the continuant posterior probability. Similar to native speakers of Spanish, L2 learners exhibit effects of stress, voicing, and place of articulation on lenition. These results suggest that all learners exhibit lenition of stops as a fricative, but more advanced learners also exhibit lenition as a sonorant. Additionally, lenition in L2 is found to be gradient and influenced by linguistic factors. Moreover, the posterior probabilities of the continuant and sonorant phonological features, estimated by the Phonet model, serve as reliable measures of lenition. Overall, this study reveals the role of proficiency and linguistic factors in shaping the degree of lenition and highlights the effectiveness of the posterior probabilities obtained from the Phonet model in quantifying lenition.
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Quantitative Acoustic versus Deep Learning Metrics of Lenition
Spanish voiced stops /b, d, ɡ/ surfaced as fricatives [β, ð, ɣ] in intervocalic position due to a phonological process known as spirantization or, more broadly, lenition. However, conditioned by various factors such as stress, place of articulation, flanking vowel quality, and speaking rate, phonetic studies reveal a great deal of variation and gradience of these surface forms, ranging from fricative-like to approximant-like [β⊤, ð⊤, ɣ⊤]. Several acoustic measurements have been used to quantify the degree of lenition, but none is standard. In this study, the posterior probabilities of sonorant and continuant phonological features in a corpus of Argentinian Spanish estimated by a deep learning Phonet model as measures of lenition were compared to traditional acoustic measurements of intensity, duration, and periodicity. When evaluated against known lenition factors: stress, place of articulation, surrounding vowel quality, word status, and speaking rate, the results show that sonorant and continuant posterior probabilities predict lenition patterns that are similar to those predicted by relative acoustic intensity measures and are in the direction expected by the effort-based view of lenition and previous findings. These results suggest that Phonet is a reliable alternative or additional approach to investigate the degree of lenition.
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- Award ID(s):
- 2037266
- PAR ID:
- 10444286
- Date Published:
- Journal Name:
- Languages
- Volume:
- 8
- Issue:
- 2
- ISSN:
- 2226-471X
- Page Range / eLocation ID:
- 98
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Predictions of gradient degree of lenition of voiceless and voiced stops in a corpus of Argentine Spanish are evaluated using three acoustic measures (minimum and maximum intensity velocity and duration) and two recurrent neural network (Phonet) measures (posterior probabilities of sonorant and continuant phonological features). While mixed and inconsistent predictions were obtained across the acoustic metrics, sonorant and continuant probability values were consistently in the direction predicted by known factors of a stop's lenition with respect to its voicing, place of articulation, and surrounding contexts. The results suggest the effectiveness of Phonet as an additional or alternative method of lenition measurement. Furthermore, this study has enhanced the accessibility of Phonet by releasing the trained Spanish Phonet model used in this study and a pipeline with step-by-step instructions for training and inferencing new models.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/languages8020098
