vowelsparse

Vowelsparse is a framework in computational phonology and natural language processing for representing vowels as sparse feature vectors rather than as dense phoneme indices. It aims to capture the essential phonetic properties of vowels—primarily height, backness, rounding, and tenseness—in a compact, high-dimensional space where many dimensions are zero for any given vowel. The approach supports cross-linguistic comparison and resource-efficient modeling, especially for systems with large vowel inventories or limited training data.

In a typical vowelsparse representation, each vowel is encoded by a subset of binary or real-valued features. Features commonly include height (close, mid, open), backness (front, central, back), rounding (rounded, unrounded), and sometimes tenseness. Additional features such as nasalization or ATR may be included depending on the task. The sparsity arises because only a small subset of features is activated for any vowel, yielding a sparse vector.

Constructing the vectors can involve expert linguistic annotation, data-driven feature selection, or automated phonetic measurements. Methods to induce sparsity include L1 regularization, feature hashing, or thresholding continuous phonetic cues. The result is a representation that can be fed to machine learning models for tasks like vowel duration estimation, phoneme classification, or cross-language typology.

Benefits include improved interpretability, better generalization in low-resource settings, and easier integration with other sparse linguistic features. Challenges include ensuring feature definitions align across languages, handling diacritics, and preserving perceptual relevance when reducing dimensionality.

See also: feature-based phonology, vowel inventory, sparse coding, vector space model, phoneme, cross-linguistic typology.