arXiv:2606.12911v1 Announce Type: new Abstract: Cascaded speech translation (ST) systems suffer from error propagation when Automatic Speech Recognition (ASR) outputs incorrect transcripts. We present the first systematic categorization of ASR errors for Vietnamese ST, classifying substitution errors by phonetic cause and quantifying their impact on downstream Neural Machine Translation (NMT) performance using Linear Mixed-Effects Modelling. We confirm that most ASR substitution errors arise from phonetic confusions rather than random noise, and that these phonetic errors significantly degrade
The increasing demand for robust speech translation in diverse languages like Vietnamese drives research into mitigating ASR errors which are a major bottleneck.
Improving speech translation accuracy for languages with complex phonetics enhances AI accessibility and utility, particularly for non-English speakers and potentially for sovereign AI initiatives.
This research provides a systematic method to categorize and address phonetic errors in ASR, leading to more robust cascaded speech translation systems for less-resourced languages.
- · AI developers
- · Vietnamese language tech users
- · Multilingual communication platforms
- · Natural Language Processing researchers
- · Companies reliant on less accurate, generic ST models
Speech translation systems for Vietnamese will become significantly more accurate, improving user experience and data quality.
This methodology could be generalized to other phonetically complex languages, accelerating the development of high-quality multilingual ST systems.
Enhanced, reliable speech translation for diverse languages could foster greater digital inclusion and cross-cultural information flow, potentially impacting geopolitical dynamics where language barriers are significant.
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Read at arXiv cs.CL