From sonority hierarchy to posterior probability as a measure of lenition: The case of Spanish stops

Tang, Kevin; Wayland, Ratree; Wang, Fenqi; Vellozzi, Sophia; Sengupta, Rahul; Altmann, Lori J. P.

doi:10.1121/10.0017247

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Once trained, posterior probabilities for different phonological features of the target segments can be computed by the model. Phonet has been found to be highly accurate in quantifying degree of lenition in Spanish [7,15,21,22] and modelling the speech impairments of patients diagnosed with Parkinson's disease [11].…”

Section: Phonetmentioning

confidence: 99%

Measuring Gradient Effects of Alcohol on Speech with Neural Networks’ Posterior Probability of Phonological Features

Tang¹,

Wang²,

Vellozzi³

et al. 2023

Preprint

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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 phonologically-informed neural network approach to estimate degrees of deaffrication of /tʃ/ and /ʤ/, spirantizationof /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 posteriorprobabilities 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.

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Section: Phonetmentioning

confidence: 99%

Measuring Gradient Effects of Alcohol on Speech with Neural Networks’ Posterior Probability of Phonological Features

Tang¹,

Wang²,

Vellozzi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Evaluating the consistency of lenition measures: Neural networks' posterior probability, intensity velocity, and duration

Tang,

Wayland,

Wang

et al. 2024

The Journal of the Acoustical Society of America

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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.

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Quantifying Lenition as a Diagnostic Marker for Parkinson’s Disease and Atypical Parkinsonism

Wayland,

Meyer,

Reddy

et al. 2024

BioMedInformatics

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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.

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From sonority hierarchy to posterior probability as a measure of lenition: The case of Spanish stops

Cited by 5 publications

References 39 publications

Measuring Gradient Effects of Alcohol on Speech with Neural Networks’ Posterior Probability of Phonological Features

Measuring Gradient Effects of Alcohol on Speech with Neural Networks’ Posterior Probability of Phonological Features

Evaluating the consistency of lenition measures: Neural networks' posterior probability, intensity velocity, and duration

Quantifying Lenition as a Diagnostic Marker for Parkinson’s Disease and Atypical Parkinsonism

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