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non_semantic_speech_benchmark

Towards Learning a Universal Non-Semantic Representation of Speech

Paper: Towards Learning a Universal Non-Semantic Representation of Speech

Things you can do

  1. Reproduce the results from our paper
  2. Compute performance of a new embedding on the Non-Semantic Speech Benchmark (NOSS)
  3. Run our embedding TRILL, or any of the other embedding networks on a new dataset.

Citation

To use this benchmark or embeddings, please cite as follows:

@article{shor2020,
    title={Towards Learning a Universal Non-Semantic Representation of Speech},
    author={Joel Shor and Aren Jansen and Ronnie Maor and Oran Lang and Omry Tuval and Felix de Chaumont Quitry and Marco Tagliasacchi and Ira Shavitt and Dotan Emanuel and Yinnon Haviv},
    year={2020},
    journal = {ArXiv e-prints},
    eprint={2002.12764},
    archivePrefix={arXiv},
    primaryClass={eess.AS},
    url = {https://arxiv.org/abs/2002.12764}
}

For questions reach out to

Joel Shor (joelshor@google.com)

Oran Lang (oranl@google.com)

Overview

Data flowchart

Embedding flowchart

Eval model flowchart