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Brahmic Schwa-Deletion with Neural Classifiers: Experiments with Bengali

The Brahmic family of writing systems is an alpha-syllabary, in which a consonant letter without an explicit vowel marker can be ambiguous: it can either represent a consonant phoneme or a CV syllable with an inherent vowel (“schwa”). The schwa-deletion ambiguity must be resolved when converting from text to an accurate phonemic representation, particularly for text-to-speech synthesis. We situate the problem of Bengali schwa-deletion in the larger context of grapheme-to-phoneme conversion for Brahmic scripts and solve it using neural network classiﬁers with graphemic features that are independent of the script and the language. Classiﬁer training is implemented using TensorFlow and related tools. We analyze the impact of both training data size and trained model size, as these represent real-life data collection and system deployment constraints. Our method achieves high accuracy for Bengali and is applicable to other languages written with Brahmic scripts.