{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/speaker-diarization-using-recurrent-neural","title":"Speaker Diarization: Using Recurrent Neural Networks","arxiv_id":"2006.05596","date":"2020-06-10","proceeding":null,"authors":[],"abstract":"Speaker Diarization is the problem of separating speakers in an audio. There\ncould be any number of speakers and final result should state when speaker\nstarts and ends. In this project, we analyze given audio file with 2 channels\nand 2 speakers (on separate channel). We train Neural Network for learning when\na person is speaking. We use different type of Neural Networks specifically,\nSingle Layer Perceptron (SLP), Multi Layer Perceptron (MLP), Recurrent Neural\nNetwork (RNN) and Convolution Neural Network (CNN) we achieve $\\sim$92\\% of\naccuracy with RNN. The code for this project is available at\nhttps://github.com/vishalshar/SpeakerDiarization_RNN_CNN_LSTM","url_abs":"http://arxiv.org/abs/2006.05596v1","url_pdf":"http://arxiv.org/pdf/2006.05596v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"speaker-diarization-using-recurrent-neural","repo_url":"https://github.com/vishalshar/SpeakerDiarization_RNN_CNN_LSTM","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"speaker-diarization","task_name":"Speaker Diarization"},{"task_slug":"speaker-diarization","task_name":"speaker-diarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}