{"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/exploring-wav2vec-2-0-fine-tuning-for","title":"Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition","arxiv_id":"2110.06309","date":"2021-10-12","proceeding":null,"authors":["Li-Wei Chen","Alexander Rudnicky"],"abstract":"While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies. Two baseline methods, vanilla fine-tuning (V-FT) and task adaptive pretraining (TAPT) are first presented. We show that V-FT is able to outperform state-of-the-art models on the IEMOCAP dataset. TAPT, an existing NLP fine-tuning strategy, further improves the performance on SER. We also introduce a novel fine-tuning method termed P-TAPT, which modifies the TAPT objective to learn contextualized emotion representations. Experiments show that P-TAPT performs better than TAPT, especially under low-resource settings. Compared to prior works in this literature, our top-line system achieved a 7.4\\% absolute improvement in unweighted accuracy (UA) over the state-of-the-art performance on IEMOCAP. Our code is publicly available.","url_abs":"https://arxiv.org/abs/2110.06309v3","url_pdf":"https://arxiv.org/pdf/2110.06309v3.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":"exploring-wav2vec-2-0-fine-tuning-for","repo_url":"https://github.com/b04901014/FT-w2v2-ser","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2110.06309","atlas_url":"https://app.syntology.ai/?focus=2110.06309","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.06309"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/b04901014/FT-w2v2-ser","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":5,"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":7,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"89eb1c45a5dc8589","entry":"RandomBucketSampler","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"pretrain/dataloader.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/pretrain/dataloader.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"89eb1c45a5dc8589"}},{"code_sha256_prefix":"4991a053c117f1d4","entry":"StandardSampler","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"pretrain/dataloader.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/pretrain/dataloader.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"4991a053c117f1d4"}},{"code_sha256_prefix":"afff10ffa9f79fa6","entry":"eval_kmeans","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"cluster.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/cluster.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"afff10ffa9f79fa6"}},{"code_sha256_prefix":"380f5345d4f504c2","entry":"multilabel2vec","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"utils/helper_funcs.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/utils/helper_funcs.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"380f5345d4f504c2"}},{"code_sha256_prefix":"43dd2f576a5240b7","entry":"prepare_mask","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"modules/FeatureFuser.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/modules/FeatureFuser.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"43dd2f576a5240b7"}},{"code_sha256_prefix":"ca8d4b1715a4e662","entry":"tonumpy","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"utils/helper_funcs.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/utils/helper_funcs.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ca8d4b1715a4e662"}},{"code_sha256_prefix":"15363f2d4c3c84df","entry":"train_kmeans","repo":"b04901014/FT-w2v2-ser","repo_kind":"official","path":"cluster.py","file_url":"https://github.com/b04901014/FT-w2v2-ser/blob/HEAD/cluster.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"15363f2d4c3c84df"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}