{"id":"W4408354505","doi":"10.1109/icassp49660.2025.10888920","title":"Effective and Efficient Mixed Precision Quantization of Speech Foundation Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Quantization (signal processing); Foundation (evidence); Speech recognition; Artificial intelligence; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000290839,0.00005811552,0.00009826488,0.0001781912,0.00004979788,0.00005552188,0.0001231823,0.00003696745,0.00001469872],"category_scores_gemma":[0.0001026768,0.00004895142,0.00002580298,0.0003605863,0.00002129369,0.0001790925,0.00007953506,0.00002610289,0.00001000056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000191285,"about_ca_system_score_gemma":0.00002060628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001927055,"about_ca_topic_score_gemma":0.000007484054,"domain_scores_codex":[0.9993691,0.00007273832,0.0001508642,0.0001990255,0.0001406733,0.00006756081],"domain_scores_gemma":[0.9993683,0.0002601762,0.00004765771,0.000164475,0.000138969,0.00002046321],"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":[0.00001323217,0.0000379069,0.00005627507,0.00001411497,0.000008064621,2.693856e-7,0.00008018041,0.0003917611,0.002029918,0.1134752,0.00005205517,0.883841],"study_design_scores_gemma":[0.0003226979,0.00002864223,0.002795428,0.00004310969,0.000006044347,0.000001488842,0.00002417686,0.848381,0.1278483,0.02041855,0.00007676152,0.00005380063],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1612198,0.00001920445,0.8259596,0.0001794392,0.0001488554,0.0002372437,3.401963e-7,0.00005002438,0.01218549],"genre_scores_gemma":[0.9186336,0.00001310118,0.0810738,0.00004759886,0.00000535188,0.00001058874,0.000001622384,0.000001831148,0.0002124594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8837873,"threshold_uncertainty_score":0.1996181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01498416403745051,"score_gpt":0.2609719196665165,"score_spread":0.245987755629066,"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."}}