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Computer Science > Computation and Language

arXiv:2409.08107 (cs)
[Submitted on 12 Sep 2024]

Title:WhisperNER: Unified Open Named Entity and Speech Recognition

Authors:Gil Ayache, Menachem Pirchi, Aviv Navon, Aviv Shamsian, Gill
Hetz, Joseph Keshet
View a PDF of the paper titled WhisperNER: Unified Open Named Entity
and Speech Recognition, by Gil Ayache and 5 other authors
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    Abstract:Integrating named entity recognition (NER) with
    automatic speech recognition (ASR) can significantly enhance
    transcription accuracy and informativeness. In this paper, we
    introduce WhisperNER, a novel model that allows joint speech
    transcription and entity recognition. WhisperNER supports
    open-type NER, enabling recognition of diverse and evolving
    entities at inference. Building on recent advancements in open
    NER research, we augment a large synthetic dataset with synthetic
    speech samples. This allows us to train WhisperNER on a large
    number of examples with diverse NER tags. During training, the
    model is prompted with NER labels and optimized to output the
    transcribed utterance along with the corresponding tagged
    entities. To evaluate WhisperNER, we generate synthetic speech
    for commonly used NER benchmarks and annotate existing ASR
    datasets with open NER tags. Our experiments demonstrate that
    WhisperNER outperforms natural baselines on both out-of-domain
    open type NER and supervised finetuning.

Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2409.08107 [cs.CL]
          (or arXiv:2409.08107v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2409.08107
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Aviv Navon [view email]
[v1] Thu, 12 Sep 2024 15:00:56 UTC (1,796 KB)
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