{"id":"W4245127862","doi":"10.24908/iqurcp.8574","title":"How Looking While Listening Affects Speech Segmentation","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Active listening; Segmentation; Speech segmentation; Context (archaeology); Natural (archaeology); Task (project management); Psychology; Cognitive psychology; Computer science; Audio visual; Speech recognition; Text segmentation; Linguistics; Natural language processing; Communication; Artificial intelligence; Multimedia; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006336765,0.0003332508,0.0003073883,0.0002698373,0.0003936034,0.001450129,0.0003379351,0.0009369579,0.006188349],"category_scores_gemma":[0.011898,0.0002887047,0.0003080718,0.0001224809,0.0006499432,0.0008400508,0.0006457963,0.0006284904,0.0006751276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002338485,"about_ca_system_score_gemma":0.0002214867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001152118,"about_ca_topic_score_gemma":0.001614275,"domain_scores_codex":[0.999169,0.0002869006,0.00006150889,0.0001827811,0.0001768358,0.0001230426],"domain_scores_gemma":[0.9936373,0.004489454,0.0006422597,0.0002463456,0.0003130403,0.0006716137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.004785076,0.00109351,0.09551709,0.0005484343,0.0001395816,0.001780288,0.01663607,0.0007284053,0.7949755,0.0007663061,0.001792457,0.08123737],"study_design_scores_gemma":[0.0003209434,0.007619882,0.8824555,0.0001955303,0.0006274257,0.00231771,0.01229413,0.003526388,0.08134749,0.003675441,0.005426725,0.0001928035],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945437,0.0001705633,0.001224857,0.0001979399,0.00005504419,0.00002073086,0.00005756794,0.0000816707,0.003647985],"genre_scores_gemma":[0.995715,0.0001558902,0.002041048,0.0002663624,0.00002156549,0.00003602555,0.00009896106,0.00005877334,0.001606372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006188349,"threshold_uncertainty_score":0.02070212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08973062391492762,"score_gpt":0.3474432551572023,"score_spread":0.2577126312422747,"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."}}