# Analyzing Error Propagation in Korean Spoken QA with ASR-LLM Cascades

arXiv: https://arxiv.org/abs/2605.17443 · HTML: https://mlsanity.com/paper/2605.17443/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 17 May 2026.

## Builds on (most cited first, 16 of 16)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling](https://mlsanity.com/paper/2312.15166/) (2023)
- [A Survey on Dialogue Systems: Recent Advances and New Frontiers](https://mlsanity.com/paper/1711.01731/) (2017)
- [WavLLM: Towards Robust and Adaptive Speech Large Language Model](https://mlsanity.com/paper/2404.00656/) (2024)
- [WavChat: A Survey of Spoken Dialogue Models](https://mlsanity.com/paper/2411.13577/) (2024)
- [AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs](https://mlsanity.com/paper/2311.06753/) (2023)
- [EXAONE 3.5: Series of Large Language Models for Real-world Use Cases](https://mlsanity.com/paper/2412.04862/) (2024)
- [Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems](https://mlsanity.com/paper/2112.05842/) (2021)
- [MoqaGPT : Zero-Shot Multi-modal Open-domain Question Answering with Large Language Model](https://mlsanity.com/paper/2310.13265/) (2023)
- [Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data](https://mlsanity.com/paper/2509.15389/) (2025)
- [DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality](https://mlsanity.com/paper/2508.18918/) (2025)

## Built on by (most cited first, 1 of 1)

- [Mind the Accent Gap: British Accent Robustness in Speech-Driven Financial Voice Assistants](https://mlsanity.com/paper/2610.06587/) (2026)

## Abstract (arXiv)

We analyze how automatic speech recognition (ASR) errors propagate through ASR--LLM cascades in Korean spoken question answering (SQA), focusing on downstream semantic failures that conventional ASR metrics cannot fully capture. Our analysis shows that the relative downstream degradation caused by ASR errors is consistent across LLMs with different absolute performance, suggesting that cascade degradation largely tracks ASR-stage information loss. We further identify single-character ASR errors as a particularly salient source of information loss in Korean, where even a minimal transcription difference can change the intended question and degrade downstream QA performance. Finally, an auxiliary comparison shows that a large audio language model outperforms an ASR--LLM cascade with an approximately matched language backbone in noisy Korean SQA, indicating the potential of direct audio input to mitigate transcript-induced information loss.