Summary of Whisperner: Unified Open Named Entity and Speech Recognition, by Gil Ayache et al.
WhisperNER: Unified Open Named Entity and Speech Recognition
by Gil Ayache, Menachem Pirchi, Aviv Navon, Aviv Shamsian, Gill Hetz, Joseph Keshet
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed WhisperNER model integrates named entity recognition (NER) and automatic speech recognition (ASR) to enhance transcription accuracy and informativeness. By supporting open-type NER, it enables the recognition of diverse and evolving entities during inference. The model is trained on a large synthetic dataset with diverse NER tags, prompting the output of transcribed utterances along with corresponding tagged entities. WhisperNER outperforms natural baselines in both out-of-domain open type NER and supervised fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WhisperNER is a new way to improve speech recognition by also recognizing important words or phrases (entities). This helps make transcription more accurate and useful. The model uses lots of examples to learn, including synthetic speech and real-world data. It’s like training a super smart stenographer! WhisperNER does well on tests that compare it to other methods. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Named entity recognition » Ner » Prompting » Supervised
