Analyzing Error Propagation in Korean Spoken QA with ASR-LLM Cascades
This study investigates the propagation of automatic speech recognition (ASR) errors in Korean spoken question-answering systems that employ an ASR–large language model (LLM) cascade, and the resulting semantic failures. Through ASR error analysis, semantic failure evaluation, and comparative experiments with end-to-end audio-language models, the authors demonstrate that even single-character ASR errors can cause complete downstream QA failure. They find that information loss during ASR is the primary driver of performance degradation, and LLMs of varying capabilities exhibit similar sensitivity to such errors. The results indicate that end-to-end models directly processing audio inputs significantly outperform conventional cascaded architectures in noisy conditions, effectively mitigating semantic information loss caused by transcription errors.