{"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/learning-speaker-embedding-with-momentum","title":"Learning Speaker Embedding with Momentum Contrast","arxiv_id":"2001.01986","date":"2020-01-07","proceeding":null,"authors":["Ke Ding","Xuanji He","Guanglu Wan"],"abstract":"Speaker verification can be formulated as a representation learning task, where speaker-discriminative embeddings are extracted from utterances of variable lengths. Momentum Contrast (MoCo) is a recently proposed unsupervised representation learning framework, and has shown its effectiveness for learning good feature representation for downstream vision tasks. In this work, we apply MoCo to learn speaker embedding from speech segments. We explore MoCo for both unsupervised learning and pretraining settings. In the unsupervised scenario, embedding is learned by MoCo from audio data without using any speaker specific information. On a large scale dataset with $2,500$ speakers, MoCo can achieve EER $4.275\\%$ trained unsupervisedly, and the EER can decrease further to $3.58\\%$ if extra unlabelled data are used. In the pretraining scenario, encoder trained by MoCo is used to initialize the downstream supervised training. With finetuning on the MoCo trained model, the equal error rate (EER) reduces $13.7\\%$ relative ($1.44\\%$ to $1.242\\%$) compared to a carefully tuned baseline training from scratch. Comparative study confirms the effectiveness of MoCo learning good speaker embedding.","url_abs":"https://arxiv.org/abs/2001.01986v2","url_pdf":"https://arxiv.org/pdf/2001.01986v2.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":"learning-speaker-embedding-with-momentum","repo_url":"https://github.com/DingKe/speaker_embedding_moco","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"speaker-verification","task_name":"Speaker Verification"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"infonce","method_name":"InfoNCE"},{"method_slug":"moco","method_name":"MoCo"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}