New Method Improves Vietnamese Speech Translation by Addressing Phonetic Errors
Researchers have developed PiDA (Phonetically-Informed Data Augmentation), a technique that improves Vietnamese speech translation systems by simulating phonetic errors that automatic speech recognition commonly makes. The study found that most ASR errors in Vietnamese stem from phonetic confusions rather than random mistakes, and that training on artificially corrupted data helps systems handle these errors better. This work addresses a key weakness in cascaded speech translation systems, where errors from speech recognition compound downstream translation quality.
A research team has presented the first systematic analysis of automatic speech recognition (ASR) errors specific to Vietnamese speech translation, categorizing substitution errors by their phonetic causes and measuring their impact on translation quality. Using statistical modeling, they confirmed that phonetic confusions—rather than random noise—account for most ASR substitution errors and significantly degrade translation performance. To address this, they developed Phonetically-Informed Data Augmentation (PiDA), which generates training data by replacing words with phonetically similar alternatives using phonetic word embeddings. When models were fine-tuned on PiDA-augmented versions of the FLEURS Vietnamese-English dataset, translation quality improved by up to 2.04 BLEU points on erroneous ASR outputs, while also maintaining slight improvements on clean text. The work was accepted to INTERSPEECH 2026.
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- arXiv cs.CLCenter
PiDA: Phonetically-Informed Data Augmentation for Robust Vietnamese Speech Translation
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