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This study investigated the acquisition of lenition in Spanish voiced stops (/b, d, ɡ/) by native English speakers during a study-abroad program, focusing on individual differences and in昀氀uencing factors. Lenition, characterized by the weakening of stops into fricative-like ([β], [ð], [ɣ]) or approximant-like ([β̞], [ð̞], [ɣ̞ ]) forms, poses challenges for L2 learners due to its gradient nature and the absence of analogous approximant forms in English. Results indicated that learners aligned with native speakers in recognizing voicing as the primary cue for lenition, yet their productions diverged, favoring fricative-like over approximant-like realizations. This preference re昀氀ects the combined in昀氀uence of articulatory ease, acoustic salience, and cognitive demands. Individual variability in learners’ trajectories highlights the role of exposure to native input and sociolinguistic engagement. Learners bene昀椀tting from richer, informal interactions with native speakers showed greater alignment with native patterns, while others demonstrated more limited progress. However, native input alone was insuf昀椀cient for learners to internalize subtler distinctions such as place of articulation and stress. These 昀椀ndings emphasize the need for combining immersive experiences with targeted instructional strategies to address articulatory and cognitive challenges. This study contributes to the understanding of L2 phonological acquisition and offers insights for designing more effective language learning programs to support lenition acquisition in Spanish. 

In one aspect, the disclosure relates to a smart pseudo-palate for use in a Smart Electropalatograph (EPG) for Linguistic and Medical Applications (SELMA) system. In one aspect, the pseudo-palate is constructed from a thin, flexible polymer membrane and having an embedded electrode array. The pseudo-palate is configured to detect tongue contacts during speech while causing minimal disturbance or interference with speech motion. The disclosed pseudo-palate in the SELMA system is integrated with a microcontroller, wireless electronic module, and external readout app. The disclosure, in another aspect, relates to integration of the pseudo-palate with a smart sports/health mouth guard containing a series of sensors for monitoring head impacts, body temperature, and heart rate. The SELMA system is capable of automated detection of neurological conditions and brain injury including, but not limited to, concussion, and neurological movement disorders, using acoustic, articulatory, and other biosignals from the device using deep data analysis. 

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. 

Objective: This study aimed to evaluate lenition, a phonological process involving consonant weakening, as a diagnostic marker for differentiating Parkinson’s Disease (PD) from Atypical Parkinsonism (APD). Early diagnosis is critical for optimizing treatment outcomes, and lenition patterns in stop consonants may provide valuable insights into the distinct motor speech impairments associated with these conditions. Methods: Using Phonet, a machine learning model trained to detect phonological features, we analyzed the posterior probabilities of continuant and sonorant features from the speech of 142 participants (108 PD, 34 APD). Lenition was quantified based on deviations from expected values, and linear mixed-effects models were applied to compare phonological patterns between the two groups. Results: PD patients exhibited more stable articulatory patterns, particularly in preserving the contrast between voiced and voiceless stops. In contrast, APD patients showed greater lenition, particularly in voiceless stops, coupled with increased articulatory variability, reflecting a more generalized motor deficit. Conclusions: Lenition patterns, especially in voiceless stops, may serve as non-invasive markers for distinguishing PD from APD. These findings suggest potential applications in early diagnosis and tracking disease progression. Future research should expand the analysis to include a broader range of phonological features and contexts to improve diagnostic accuracy. 

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. 

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. 

Alcohol is known to impair fine articulatory control and movements. In drunken speech, incomplete closure of the vocal tract can result in deaffrication of the English affricate sounds /tʃ/ and /ʤ/, spirantization (fricative-like production) of the stop consonants and palatalization (retraction of place of articulation) of the alveolar fricative /s/ (produced as /ʃ/). Such categorical segmental errors have been well-reported. This study employs a phonologicallyinformed neural network approach to estimate degrees of deaffrication of /tʃ/ and /ʤ/, spirantization of /t/ and /d/ and place retraction for /s/ in a corpus of intoxicated English speech. Recurrent neural networks were trained to recognize relevant phonological features [anterior], [continuant] and [strident] in a control speech corpus. Their posterior probabilities were computed over the segments produced under intoxication. The results obtained revealed both categorical and gradient errors and, thus, suggested that this new approach could reliably quantify fine-grained errors in intoxicated speech. 

Alcohol is known to impair fine articulatory control and movements. In drunken speech, incomplete closure of the vocal tract can result in deaffrication of the English affricate sounds /tʃ/ and /ʤ/, spirantization (fricative-like production) of the stop consonants and palatalization (retraction of place of articulation) of the alveolar fricative /s/ (produced as /ʃ/). Such categorical segmental errors have been well-reported. This study employs a phonologicallyinformed neural network approach to estimate degrees of deaffrication of /tʃ/ and /ʤ/, spirantization of /t/ and /d/ and place retraction for /s/ in a corpus of intoxicated English speech. Recurrent neural networks were trained to recognize relevant phonological features [anterior], [continuant] and [strident] in a control speech corpus. Their posterior probabilities were computed over the segments produced under intoxication. The results obtained revealed both categorical and gradient errors and, thus, suggested that this new approach could reliably quantify fine-grained errors in intoxicated speech. Keywords: alcohol, deaffrication, palatalization, retraction, neural network. 

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.