{"meta":{"query_hash":"7dc0eb68760f","filters":{"venue":"2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/7dc0eb68760f","api":"https://metacan.xera.ac/api/v1/cohort?venue=2021+IEEE+Automatic+Speech+Recognition+and+Understanding+Workshop+%28ASRU%29"},"results":[{"id":"W4210486131","doi":"10.1109/asru51503.2021.9687877","title":"Hybrid Network with Multi-Level Global-Local Statistics Pooling for Robust Text-Independent Speaker Recognition","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Pooling; NIST; Speech recognition; Speaker recognition; Context (archaeology); Artificial intelligence; Set (abstract data type); Speaker diarisation; Hybrid system; Pattern recognition (psychology); Machine learning","score_opus":0.14976837305959667,"score_gpt":0.283479670430947,"score_spread":0.13371129737135035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210486131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04235844,0.0017842634,0.9480122,0.0002030893,0.00026763877,0.00010083425,0.0003147678,0.0043385425,0.002620224],"genre_scores_gemma":[0.654555,0.0007941129,0.33046082,0.00047916695,0.000347242,0.00024702054,0.0014436992,0.00030512974,0.0113677895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938285,0.000113361806,0.000030220977,0.0002540043,0.00013382148,0.00008574201],"domain_scores_gemma":[0.99968386,0.00009609335,0.00002949355,0.000054926077,0.00011324205,0.000022527647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012222246,0.0013433606,0.00079881365,0.0006930128,0.00037521368,0.0006018396,0.0013288998,0.000814872,0.0027089461],"category_scores_gemma":[0.0010902149,0.0003893809,0.00073957787,0.00055090006,0.00037618002,0.0018712549,0.001296087,0.000904327,0.0016208135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010448004,0.00026739814,0.0013990672,0.00021465111,0.00048601683,0.00027273304,0.00018429638,0.053512536,0.17570485,0.0025123311,0.0067298673,0.7576715],"study_design_scores_gemma":[0.000028791215,0.00027218464,0.0021357967,0.00001856599,0.0002078889,0.000223693,0.00004092178,0.9297901,0.061016273,0.002269837,0.0039314386,0.00006454732],"about_ca_topic_score_codex":0.0034865427,"about_ca_topic_score_gemma":0.005258228,"teacher_disagreement_score":0.0034865427,"about_ca_system_score_codex":0.0004488307,"about_ca_system_score_gemma":0.00044883107,"threshold_uncertainty_score":0.00906229},"labels":[],"label_agreement":null}]}