Beyond Phones: Structured Phonemic Modeling for Vietnamese Automatic Speech Recognition

Abstract

Phone-based representations provide a compact and acoustically grounded alternative to conventional orthographic modeling for automatic speech recognition (ASR). However, phones describe surface pronunciations and may lose lexical distinctions under dialect-dependent sound mergers, making their conversion back to orthographic text inherently ambiguous. This issue is particularly relevant to Vietnamese, where pronunciation varies considerably across regional dialects. This work proposes a structured phonemic approach to Vietnamese ASR that moves the output representation from surface phones to abstract phonemes. Exploiting the regular phoneme-grapheme correspondence of Vietnamese, each syllable is represented by a phonemic triplet consisting of its initial, rhyme, and tone, preserving lexical distinctions while enabling deterministic reconstruction of orthographic text. We further introduce a \textbf{Phonemic Syllabic-Structure Decoder} that captures the hierarchical organization of Vietnamese syllables by first predicting the rhyme and subsequently conditioning the initial and tone predictions on the rhyme. Experiments on the standard LSVSC and multi-dialect UIT-ViMD benchmarks demonstrate the effectiveness of the proposed approach. The best models achieve WERs of 5.83\% on LSVSC and 12.58\% on UIT-ViMD, outperforming orthographic, phonetic, and previous phonemic approaches. Further analyses reveal broader lexical coverage, reduced dependence on word-frequency patterns, and consistent behavior across Vietnamese dialects. These results demonstrate the effectiveness of moving from phonetic to structured phonemic modeling and highlight the importance of incorporating language-specific phonological structure into end-to-end ASR.