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LLM matchers outperform embeddings for topic matching in noisy ASR transcripts

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a benchmark and dataset for topic matching in real-world Automatic Speech Recognition (ASR) transcripts from call centers. The study compares three types of matchers: regex-based, zero-shot sentence embedding encoders, and Gemini-based LLM matchers, evaluating both keyphrase and natural language description topic representations. Results indicate that lightweight LLM matchers perform best when using natural language descriptions for topics. AI

IMPACT This research could improve the accuracy of agent-assist tools in call centers by enhancing topic identification in noisy ASR transcripts.

RANK_REASON The cluster contains an academic paper detailing a new benchmark and experimental results for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM matchers outperform embeddings for topic matching in noisy ASR transcripts

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26 / 100
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The cluster contains an academic paper detailing a new benchmark and experimental results for a specific AI task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Saman Rahbar, Xiliang Zhu, Irvin Cardoza, David Rossouw ·

    Topic Matching in the Wild: Benchmark and Lessons from Real-World ASR Transcripts

    arXiv:2609.00330v1 Announce Type: cross Abstract: In contact centers, real-time agent-assist tools determine, for each of many predefined topics, whether a live customer utterance is relevant and display a coaching card to the agent when it is. The input is noisy and challenging:…

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