{"id":"W2137997377","doi":"10.1109/icassp.2013.6639126","title":"Multiple windowed spectral features for emotion recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Institut National de la Recherche Scientifique; Computer Research Institute of Montréal","funders":"","keywords":"Multitaper; Mel-frequency cepstrum; Speech recognition; Computer science; Pattern recognition (psychology); Mixture model; Dynamic time warping; Feature (linguistics); Set (abstract data type); Artificial intelligence; Cepstrum; Feature extraction","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001034433,0.00008856449,0.00008996369,0.00008673443,0.00009307516,0.0002031487,0.000211222,0.00005766709,0.0006537815],"category_scores_gemma":[0.000138004,0.00007403219,0.00008809919,0.0001251979,0.00001317816,0.0005958974,0.00002500247,0.00004840945,0.0008872403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002081939,"about_ca_system_score_gemma":0.00001314346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005244083,"about_ca_topic_score_gemma":0.00002171676,"domain_scores_codex":[0.9992905,0.00002822615,0.0001278546,0.0002392952,0.0001150438,0.0001990565],"domain_scores_gemma":[0.9994035,0.0001720393,0.00003977252,0.0001808093,0.000130862,0.00007304487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005858846,0.00006985826,0.000188232,0.000007688188,0.00001247013,7.576224e-7,0.0001059259,6.17271e-7,0.00685177,0.001777841,0.01746707,0.9735119],"study_design_scores_gemma":[0.003087262,0.000373711,0.1532176,0.00005718426,0.00002383178,0.0001005967,0.0003842521,0.1133111,0.579287,0.1421162,0.007053991,0.0009872828],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.116876,0.00001277374,0.8558059,0.002724757,0.0005228962,0.0008595279,0.000006947152,0.0005100697,0.0226812],"genre_scores_gemma":[0.5339994,0.000005511612,0.4623965,0.0009873653,0.0001495467,0.0001436871,0.00002180674,0.000008700265,0.002287478],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9725246,"threshold_uncertainty_score":0.9998907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02811234192965691,"score_gpt":0.2315257983846509,"score_spread":0.203413456454994,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}