ViSpeechFormer: A Phonemic Approach for Vietnamese Automatic Speech Recognition
This addresses speech recognition for Vietnamese speakers, offering a novel framework that could benefit other phonetic languages, though it is incremental as it adapts existing transformer methods to a specific linguistic context.
The paper tackled Vietnamese automatic speech recognition by proposing ViSpeechFormer, a phoneme-based approach that exploits the language's phonetic orthography, achieving strong performance and better generalization to out-of-vocabulary words on two public datasets.
Vietnamese has a phonetic orthography, where each grapheme corresponds to at most one phoneme and vice versa. Exploiting this high grapheme-phoneme transparency, we propose ViSpeechFormer (\textbf{Vi}etnamese \textbf{Speech} Trans\textbf{Former}), a phoneme-based approach for Vietnamese Automatic Speech Recognition (ASR). To the best of our knowledge, this is the first Vietnamese ASR framework that explicitly models phonemic representations. Experiments on two publicly available Vietnamese ASR datasets show that ViSpeechFormer achieves strong performance, generalizes better to out-of-vocabulary words, and is less affected by training bias. This phoneme-based paradigm is also promising for other languages with phonetic orthographies. The code will be released upon acceptance of this paper.