{"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/deep-learning-based-non-intrusive-multi","title":"Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features","arxiv_id":"2111.02363","date":"2021-11-03","proceeding":null,"authors":["Ryandhimas E. Zezario","Szu-Wei Fu","Fei Chen","Chiou-Shann Fuh","Hsin-Min Wang","Yu Tsao"],"abstract":"In this study, we propose a cross-domain multi-objective speech assessment model called MOSA-Net, which can estimate multiple speech assessment metrics simultaneously. Experimental results show that MOSA-Net can improve the linear correlation coefficient (LCC) by 0.026 (0.990 vs 0.964 in seen noise environments) and 0.012 (0.969 vs 0.957 in unseen noise environments) in perceptual evaluation of speech quality (PESQ) prediction, compared to Quality-Net, an existing single-task model for PESQ prediction, and improve LCC by 0.021 (0.985 vs 0.964 in seen noise environments) and 0.047 (0.836 vs 0.789 in unseen noise environments) in short-time objective intelligibility (STOI) prediction, compared to STOI-Net (based on CRNN), an existing single-task model for STOI prediction. Moreover, MOSA-Net, originally trained to assess objective scores, can be used as a pre-trained model to be effectively adapted to an assessment model for predicting subjective quality and intelligibility scores with a limited amount of training data. Experimental results show that MOSA-Net can improve LCC by 0.018 (0.805 vs 0.787) in mean opinion score (MOS) prediction, compared to MOS-SSL, a strong single-task model for MOS prediction. In light of the confirmed prediction capability, we further adopt the latent representations of MOSA-Net to guide the speech enhancement (SE) process and derive a quality-intelligibility (QI)-aware SE (QIA-SE) approach accordingly. Experimental results show that QIA-SE provides superior enhancement performance compared with the baseline SE system in terms of objective evaluation metrics and qualitative evaluation test. For example, QIA-SE can improve PESQ by 0.301 (2.953 vs 2.652 in seen noise environments) and 0.18 (2.658 vs 2.478 in unseen noise environments) over a CNN-based baseline SE model.","url_abs":"https://arxiv.org/abs/2111.02363v5","url_pdf":"https://arxiv.org/pdf/2111.02363v5.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":"deep-learning-based-non-intrusive-multi","repo_url":"https://github.com/dhimasryan/MOSA-Net-Cross-Domain","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"speech-enhancement","task_name":"Speech Enhancement"}],"methods":[{"method_slug":"lcc","method_name":"LCC"},{"method_slug":"multiplicative-attention","method_name":"Multiplicative Attention"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2111.02363","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.02363"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/dhimasryan/MOSA-Net-Cross-Domain","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"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":"48a217c7f5503ac4","entry":"ListRead","repo":"dhimasryan/MOSA-Net-Cross-Domain","repo_kind":"official","path":"MOSA_Net+/Generate_Whisper_Feature.py","file_url":"https://github.com/dhimasryan/MOSA-Net-Cross-Domain/blob/HEAD/MOSA_Net%2B/Generate_Whisper_Feature.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":"48a217c7f5503ac4"}},{"code_sha256_prefix":"1f689cfa346f0880","entry":"denorm","repo":"dhimasryan/MOSA-Net-Cross-Domain","repo_kind":"official","path":"MOSA_Net+/MOSA_Net_plus.py","file_url":"https://github.com/dhimasryan/MOSA-Net-Cross-Domain/blob/HEAD/MOSA_Net%2B/MOSA_Net_plus.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":"1f689cfa346f0880"}},{"code_sha256_prefix":"4dc04de1bc04efe4","entry":"frame_score","repo":"dhimasryan/MOSA-Net-Cross-Domain","repo_kind":"official","path":"MOSA_Net+/MOSA_Net_plus.py","file_url":"https://github.com/dhimasryan/MOSA-Net-Cross-Domain/blob/HEAD/MOSA_Net%2B/MOSA_Net_plus.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":"4dc04de1bc04efe4"}},{"code_sha256_prefix":"385188f9ee043482","entry":"shuffle_list","repo":"dhimasryan/MOSA-Net-Cross-Domain","repo_kind":"official","path":"Extracting_Hubert_Feature_VoiceMOS_Challenge.py","file_url":"https://github.com/dhimasryan/MOSA-Net-Cross-Domain/blob/HEAD/Extracting_Hubert_Feature_VoiceMOS_Challenge.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":"385188f9ee043482"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}