{"doi":"10.1109/icsp.2018.8652301","title":"Mutitask Learning Based Muti-examples Keywords Spotting in Low Resource Condition","abstract":null,"journal":"2018 14th IEEE International Conference on Signal Processing (ICSP)","year":2018,"id":683879,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1786576,"name":"Kang Jian","orcid":null,"position":1,"is_corresponding":false},{"id":1786577,"name":"Zhang Wei-Qiang","orcid":null,"position":2,"is_corresponding":false},{"id":1688480,"name":"Liu Jia","orcid":"0009-0006-2698-6543","position":3,"is_corresponding":false},{"id":1786574,"name":"Yang Jianbin","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Mutitask Learning Based Muti-examples Keywords Spotting in Low Resource Condition","abstract":"Keywords Spotting (KWS) is a critical task in speech recognition, aiming to spot the pre-selected keywords out of a continuous speech. In a typical low resource condition, we can only obtain dozens of examples of each keyword. How to make full use of multi-examples information to build an effective keyword spotting system is the present challenge since traditional keyword spotting technologies are not suitable. In this paper, we propose a multi-examples keywords spotting system, which gains a significant performance improvement by applying multitask learning technologies to extract feature and build model. A post-processing method is also used to reduce the false alarm rate.","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26207759","pmcid":null,"openalex_id":"https://openalex.org/W2918559346","authors":[],"funders":[],"total_grants":0,"fwci":0.1132,"citation_percentile":0.45662318,"influential_citations":0,"citation_trend":[{"year":2021,"count":1},{"year":2022,"count":1},{"year":2023,"count":1},{"year":2025,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/8648892/8652266/08652301.pdf?arnumber=8652301","host_type":"publisher"},{"url":"https://doi.org/10.1109/icsp.2018.8652301","host_type":""}],"fields_of_study":["Speech Recognition and Synthesis","Text and Document Classification Technologies","Topic Modeling","Computer Science"],"mesh_terms":[],"keywords":["Keyword spotting","Spotting","Computer science","ALARM","Feature (linguistics)","Task (project management)","Artificial intelligence","Resource (disambiguation)","Speech recognition","Word (group theory)","Machine learning"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-18T12:31:13.841452Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}