{"ID":2867395,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.19567","arxiv_id":"2509.19567","title":"Retrieval Augmented Generation based context discovery for ASR","abstract":"This work investigates retrieval augmented generation as an efficient strategy for automatic context discovery in context-aware Automatic Speech Recognition (ASR) system, in order to improve transcription accuracy in the presence of rare or out-of-vocabulary terms. However, identifying the right context automatically remains an open challenge. This work proposes an efficient embedding-based retrieval approach for automatic context discovery in ASR. To contextualize its effectiveness, two alternatives based on large language models (LLMs) are also evaluated: (1) large language model (LLM)-based context generation via prompting, and (2) post-recognition transcript correction using LLMs. Experiments on the TED-LIUMv3, Earnings21 and SPGISpeech demonstrate that the proposed approach reduces WER by up to 17% (percentage difference) relative to using no-context, while the oracle context results in a reduction of up to 24.1%.","short_abstract":"This work investigates retrieval augmented generation as an efficient strategy for automatic context discovery in context-aware Automatic Speech Recognition (ASR) system, in order to improve transcription accuracy in the presence of rare or out-of-vocabulary terms. However, identifying the right context automatically r...","url_abs":"https://arxiv.org/abs/2509.19567","url_pdf":"https://arxiv.org/pdf/2509.19567v2","authors":"[\"Dimitrios Siskos\",\"Stavros Papadopoulos\",\"Pablo Peso Parada\",\"Jisi Zhang\",\"Karthikeyan Saravanan\",\"Anastasios Drosou\"]","published":"2025-09-23T20:47:15Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"eess.AS\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}