{"id":"W3109961563","doi":"10.3390/math8122133","title":"CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network","year":2020,"lang":"en","type":"article","venue":"Mathematics","topic":"Music and Audio Processing","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Feature (linguistics); Block (permutation group theory); Machine learning; State (computer science); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003164405,0.0001268636,0.0001588723,0.00001955491,0.0002591203,0.0002453981,0.0004369494,0.00007027368,0.00003356866],"category_scores_gemma":[0.0001108475,0.00008965792,0.00006485729,0.0004172901,0.0000547287,0.0001629765,0.000107992,0.0002413868,0.00004510652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002194672,"about_ca_system_score_gemma":0.00005893479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000112381,"about_ca_topic_score_gemma":8.293096e-7,"domain_scores_codex":[0.9990176,0.00006896313,0.0002041828,0.0001919045,0.0002799627,0.0002374133],"domain_scores_gemma":[0.9992786,0.0001634967,0.0001484344,0.0002618077,0.00006581564,0.00008183312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005668711,0.0005858663,0.0004188622,0.002163943,0.0001570305,0.0001450804,0.03619998,0.0214342,0.004283445,0.03948618,0.04959319,0.8454756],"study_design_scores_gemma":[0.0001709218,0.00002682577,0.00002056784,0.0001065937,0.0000179121,0.00002125291,0.00006161749,0.9640893,0.001166016,0.03324246,0.0009419264,0.0001345718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01291196,0.00005609314,0.9773026,0.008402321,0.0001675796,0.0001612695,8.80754e-7,0.0001452281,0.0008520624],"genre_scores_gemma":[0.1650751,0.00000289095,0.8253241,0.008965629,0.0005820883,0.000004778953,0.000006493106,0.00001773002,0.00002125585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9426551,"threshold_uncertainty_score":0.3656144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06164806315143793,"score_gpt":0.2596169155525182,"score_spread":0.1979688524010803,"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."}}