{"url":"/method/frill","slug":"frill","name":"FRILL","full_name":"FRILL","full_name_withheld":false,"description_markdown":"**FRILL** is a non-semantic speech embedding model trained via knowledge distillation that is fast enough to be run in real-time on a mobile device. The fastest model runs at 0.9 ms, which is 300x faster than TRILL and 25x faster than TRILL-distilled.","description_state":"present","introduced_year":null,"introduced_by":{"title":"FRILL: A Non-Semantic Speech Embedding for Mobile Devices","paper":"/paper/frill-a-non-semantic-speech-embedding-for","first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/frill-a-non-semantic-speech-embedding-for"},"source":{"url":"https://arxiv.org/abs/2011.04609v5","title":"FRILL: A Non-Semantic Speech Embedding for Mobile Devices","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Audio","area_id":"audio","collection":"Speech Embeddings","url":"/methods/category/speech-embeddings","pwc_aliases":[]}],"n_papers_tagged":0,"archive_num_papers":1,"papers_newest_first":[],"papers_shown":0,"tasks":[],"tasks_shown":0,"n_tasks":0,"usage_by_year":[],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/frill"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}