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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06062">arXiv:2409.06062</a> <span> [<a href="https://arxiv.org/pdf/2409.06062">pdf</a>, <a href="https://arxiv.org/format/2409.06062">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Retrieval Augmented Correction of Named Entity Speech Recognition Errors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pusateri%2C+E">Ernest Pusateri</a>, <a href="/search/eess?searchtype=author&query=Walia%2C+A">Anmol Walia</a>, <a href="/search/eess?searchtype=author&query=Kashi%2C+A">Anirudh Kashi</a>, <a href="/search/eess?searchtype=author&query=Bandyopadhyay%2C+B">Bortik Bandyopadhyay</a>, <a href="/search/eess?searchtype=author&query=Hyder%2C+N">Nadia Hyder</a>, <a href="/search/eess?searchtype=author&query=Mahinder%2C+S">Sayantan Mahinder</a>, <a href="/search/eess?searchtype=author&query=Anantha%2C+R">Raviteja Anantha</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+D">Daben Liu</a>, <a href="/search/eess?searchtype=author&query=Gondala%2C+S">Sashank Gondala</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.06062v1-abstract-short" style="display: inline;"> In recent years, end-to-end automatic speech recognition (ASR) systems have proven themselves remarkably accurate and performant, but these systems still have a significant error rate for entity names which appear infrequently in their training data. In parallel to the rise of end-to-end ASR systems, large language models (LLMs) have proven to be a versatile tool for various natural language proce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06062v1-abstract-full').style.display = 'inline'; document.getElementById('2409.06062v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06062v1-abstract-full" style="display: none;"> In recent years, end-to-end automatic speech recognition (ASR) systems have proven themselves remarkably accurate and performant, but these systems still have a significant error rate for entity names which appear infrequently in their training data. In parallel to the rise of end-to-end ASR systems, large language models (LLMs) have proven to be a versatile tool for various natural language processing (NLP) tasks. In NLP tasks where a database of relevant knowledge is available, retrieval augmented generation (RAG) has achieved impressive results when used with LLMs. In this work, we propose a RAG-like technique for correcting speech recognition entity name errors. Our approach uses a vector database to index a set of relevant entities. At runtime, database queries are generated from possibly errorful textual ASR hypotheses, and the entities retrieved using these queries are fed, along with the ASR hypotheses, to an LLM which has been adapted to correct ASR errors. Overall, our best system achieves 33%-39% relative word error rate reductions on synthetic test sets focused on voice assistant queries of rare music entities without regressing on the STOP test set, a publicly available voice assistant test set covering many domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06062v1-abstract-full').style.display = 'none'; document.getElementById('2409.06062v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to ICASSP 2025</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: 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