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New Speech Retrieval Model Uses ASR Data for Efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SSEmb, a novel speech retrieval model that leverages automatic speech recognition (ASR) data instead of costly spoken query-document pairs. This approach uses a frozen speech encoder and a frozen pretrained text embedding model, with a lightweight adapter projecting speech embeddings into the retrieval space. SSEmb demonstrates competitive performance against cascaded ASR-then-retrieve systems, offering reduced inference latency and outperforming existing public speech embedding models. The model also shows versatility by transferring to text-to-speech and speech-to-speech retrieval without further training. AI

IMPACT This model could significantly reduce the cost and complexity of building voice agents capable of retrieving information from spoken queries.

RANK_REASON Academic paper detailing a new model and training methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

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

New Speech Retrieval Model Uses ASR Data for Efficiency

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Academic paper detailing a new model and training methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Sungroh Yoon ·

    SSEmb: Training a Generalizable Speech Retrieval Model Using Only Automatic Speech Recognition Data

    End-to-end speech-to-text retrieval is becoming an important capability for voice agents, allowing spoken queries to retrieve relevant documents without intermediate transcription. Training these retrievers typically depends on large collections of spoken query-document pairs tha…

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