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End-to-end Named Entity Recognition from English Speech

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arxiv 2005.11184 v1 pith:JNQP3GLW submitted 2020-05-22 cs.CL cs.LGcs.SDeess.AS

End-to-end Named Entity Recognition from English Speech

Hemant Yadav , Sreyan Ghosh , Yi Yu , Rajiv Ratn Shah This is my paper
classification cs.CL cs.LGcs.SDeess.AS
keywords speechapproachrecognitionend-to-endenglishentityfirstnamed
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Named entity recognition (NER) from text has been a widely studied problem and usually extracts semantic information from text. Until now, NER from speech is mostly studied in a two-step pipeline process that includes first applying an automatic speech recognition (ASR) system on an audio sample and then passing the predicted transcript to a NER tagger. In such cases, the error does not propagate from one step to another as both the tasks are not optimized in an end-to-end (E2E) fashion. Recent studies confirm that integrated approaches (e.g., E2E ASR) outperform sequential ones (e.g., phoneme based ASR). In this paper, we introduce a first publicly available NER annotated dataset for English speech and present an E2E approach, which jointly optimizes the ASR and NER tagger components. Experimental results show that the proposed E2E approach outperforms the classical two-step approach. We also discuss how NER from speech can be used to handle out of vocabulary (OOV) words in an ASR system.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation

    cs.CL 2025-09 conditional novelty 6.0

    NE-PADD combines SpeechNER attention with a deepfake detector via fusion or transfer, reaching 7.89% EER on the new PartialSpoof-NER benchmark.

  2. SpeechLLM: Unified Speech and Language Model for Enhanced Multi-Task Understanding in Low Resource Settings

    cs.CL 2025-08 conditional novelty 5.0

    A parameter-efficient adapter bridging Whisper and TinyLlama reports relative improvements in speech recognition, named entity recognition, and sentiment analysis on low-resource benchmarks.

  3. TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation

    cs.CL 2025-08 conditional novelty 4.0

    Adding task-specific learned vectors to acoustic embeddings lets a transducer ASR model train on partially labeled data, matching or beating the fully labeled TokenVerse baseline on most tasks.