Year: 2026
Type: article
Abstract: Spoken Question Answering (SQA) extends machine reading comprehension to spoken content and requires models to handle both automatic speech recognition (ASR) errors and downstream language understanding. Although large-scale SQA benchmarks exist for high-resource languages, Vietnamese remains underexplored due to the lack of standardized datasets. This paper introduces ViSQA, the first benchmark for Vietnamese Spoken Question Answering. ViSQA extends the UIT-ViQuAD corpus using a reproducible text-to-speech and ASR pipeline, resulting in over 13,000 question-answer pairs aligned with spoken inputs. The dataset includes clean and noise-degraded audio variants to enable systematic evaluation under varying transcription quality. Experiments with five transformer-based models show that ASR errors substantially degrade performance (e.g., ViT5 EM: 62.04% [Formula: see text] 36.30%), while training on spoken transcriptions improves robustness (ViT5 EM: 36.30% [Formula: see text] 50.70%). ViSQA provides a rigorous benchmark for evaluating Vietnamese SQA systems and enables systematic analysis of the impact of ASR errors on downstream reasoning.
Source: PLoS ONE
Authors: Le Trong Minh, Nguyen Duc Thinh, Nguyen Khanh Tho Loc, Le Van Quan, Ngo Duc Tam, Le Hoang Son
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,382
Funder: Đại học Quốc gia Hà Nội
Award: G8879977192
OpenAlex ID: W7123350710