{"id":"W4385786281","doi":"","title":"ON THE USE OF LINGUISTIC FEATURES IN AN AUTOMATIC SYSTEM FOR SPEECH ANALYTICS OF TELEPHONE CONVERSATIONS","year":2011,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Analytics; Natural language processing; Speech recognition; Speech corpus; Linguistics; Speech synthesis; Data science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002393442,0.00055038,0.0005516162,0.001059061,0.0008759433,0.002445676,0.0008318733,0.001111443,0.002377393],"category_scores_gemma":[0.01129761,0.0003562575,0.0004958306,0.0006927698,0.000537343,0.002641217,0.0009625371,0.000778679,0.001280413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004145619,"about_ca_system_score_gemma":0.0005243382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006453983,"about_ca_topic_score_gemma":0.006470598,"domain_scores_codex":[0.9984982,0.0006150356,0.0001341186,0.0003177269,0.0003125472,0.0001223808],"domain_scores_gemma":[0.9878221,0.009693177,0.0002928752,0.0005995629,0.001437361,0.0001548897],"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":[0.002974316,0.0005674953,0.010806,0.0002716151,0.0001345805,0.0005881853,0.0009377291,0.01251441,0.2367663,0.00280318,0.00412877,0.7275075],"study_design_scores_gemma":[0.0001297843,0.0009866656,0.02226643,0.00007421654,0.0002955041,0.00058832,0.0005951856,0.7807689,0.1846966,0.003294407,0.006172897,0.0001310745],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5354298,0.000608279,0.4455931,0.0009616069,0.0001975022,0.0004455165,0.001096644,0.008230176,0.007437404],"genre_scores_gemma":[0.8306657,0.0001882256,0.1645352,0.0002333364,0.00008726696,0.0001518499,0.0007617137,0.0005355858,0.002841127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006453983,"threshold_uncertainty_score":0.01283282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04479030691898692,"score_gpt":0.2359495362847823,"score_spread":0.1911592293657954,"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."}}