{"id":"W4226435591","doi":"10.3758/s13428-022-02029-6","title":"Shennong: A Python toolbox for audio speech features extraction","year":2023,"lang":"en","type":"preprint","venue":"Behavior Research Methods","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Agence Nationale de la Recherche; Institut national de recherche en informatique et en automatique (INRIA); Canadian Institute for Advanced Research; Facebook","keywords":"Computer science; Python (programming language); Speech recognition; Toolbox; Normalization (sociology); Software; Scripting language; Mel-frequency cepstrum; Speech processing; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Feature extraction; Programming language","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004465669,0.001499454,0.0006974852,0.001061274,0.0003569276,0.001026734,0.001460384,0.0005476604,0.08064564],"category_scores_gemma":[0.002063135,0.0008029033,0.001108493,0.0006994295,0.0003014432,0.0008304917,0.001332347,0.001241525,0.03698346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004391726,"about_ca_system_score_gemma":0.0009991408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003036709,"about_ca_topic_score_gemma":0.007462854,"domain_scores_codex":[0.9997502,0.00003151498,0.00002337866,0.00007167289,0.00007890502,0.00004439068],"domain_scores_gemma":[0.9995176,0.0002263459,0.00003193797,0.00009021572,0.00009713206,0.00003678551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007119912,0.0002319532,0.003332078,0.001637022,0.0003060871,0.0004420413,0.0004849257,0.01315493,0.04123142,0.01082451,0.4203069,0.5073361],"study_design_scores_gemma":[0.0005445261,0.0001746107,0.01063953,0.0002624867,0.0001730424,0.0009915909,0.0002265881,0.4334827,0.109424,0.05621196,0.3875948,0.0002741319],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.003032423,0.0001281734,0.562917,0.00009384082,0.00009659166,0.0001791775,0.01736025,0.4107504,0.005442264],"genre_scores_gemma":[0.08587504,0.000443538,0.7226987,0.0007407642,0.0001186039,0.002457603,0.04430705,0.1006932,0.04266549],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.08064564,"threshold_uncertainty_score":0.2697866,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4835932963510312,"score_gpt":0.6236894237901746,"score_spread":0.1400961274391433,"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."}}