{"id":"W2912129510","doi":"10.1109/icassp.2019.8683565","title":"Exploring Attention Mechanism for Acoustic-based Classification of Speech Utterances into System-directed and Non-system-directed","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Phrase; Mechanism (biology); Speech recognition; Word (group theory); Convolutional neural network; Artificial intelligence; Recurrent neural network; Natural language processing; Artificial neural network; Linguistics","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":[0.001477043,0.0009961863,0.0005299101,0.0006007453,0.0003068486,0.0007561666,0.001136167,0.001007676,0.00128093],"category_scores_gemma":[0.002587711,0.0002949175,0.0006994958,0.0003374326,0.0004686456,0.001159204,0.0007753219,0.001422999,0.0004935441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009180807,"about_ca_system_score_gemma":0.0007874806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00958624,"about_ca_topic_score_gemma":0.007826187,"domain_scores_codex":[0.9994875,0.0001305398,0.00002801748,0.0001961426,0.00006365718,0.00009411386],"domain_scores_gemma":[0.9988378,0.0006349945,0.00009528375,0.0001094631,0.000270439,0.0000520957],"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":[0.0012122,0.0005272349,0.009992444,0.0002612579,0.0003406266,0.0002672256,0.0004483777,0.2392253,0.1119498,0.004539838,0.002909365,0.6283265],"study_design_scores_gemma":[0.000007148945,0.000119761,0.002199465,0.000008843848,0.00004829192,0.00003692785,0.00002611385,0.9834328,0.01249558,0.001252739,0.0003617896,0.0000106513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3374988,0.001797923,0.6538905,0.000640406,0.0002051365,0.0001025302,0.0002067013,0.002306681,0.003351305],"genre_scores_gemma":[0.963257,0.0003150086,0.03213834,0.0001553886,0.0000713788,0.00005185727,0.0002560481,0.00006006869,0.003695001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00958624,"threshold_uncertainty_score":0.01906091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08203991703136106,"score_gpt":0.2662062726164808,"score_spread":0.1841663555851198,"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."}}