{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:00:42Z","timestamp":1782313242551,"version":"3.54.5"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Intelligence Advanced Research Projects Activity via Department of Defense U.S. Army Research Laboratory","award":["W911NF-12-C-0012"],"award-info":[{"award-number":["W911NF-12-C-0012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1109\/jstsp.2017.2759726","type":"journal-article","created":{"date-parts":[[2017,10,5]],"date-time":"2017-10-05T18:15:54Z","timestamp":1507227354000},"page":"1351-1359","source":"Crossref","is-referenced-by-count":61,"title":["End-to-End ASR-Free Keyword Search From Speech"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2340-1144","authenticated-orcid":false,"given":"Kartik","family":"Audhkhasi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1780-4390","authenticated-orcid":false,"given":"Andrew","family":"Rosenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abhinav","family":"Sethy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8049-2345","authenticated-orcid":false,"given":"Bhuvana","family":"Ramabhadran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1343-6837","authenticated-orcid":false,"given":"Brian","family":"Kingsbury","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref32","first-page":"10","article-title":"Scalable minimum Bayes risk training of deep neural network acoustic models using distributed\n Hessian-free optimization","author":"kingsbury","year":"0","journal-title":"Proc INTERSPEECH"},{"key":"ref31","first-page":"735","article-title":"Deep learning via Hessian-free optimization","author":"martens","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref30","first-page":"630","article-title":"Generalization and parameter estimation in feedforward nets: Some experiments","author":"morgan","year":"0","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472618"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2017-1566"},{"key":"ref12","first-page":"10","article-title":"Fast and accurate recurrent neural network acoustic\n models for speech recognition","author":"sak","year":"0","journal-title":"Proc INTERSPEECH"},{"key":"ref13","first-page":"959","article-title":"Direct acoustics-to-word models for English conversational speech\n recognition","author":"audhkhasi","year":"0","journal-title":"Proc INTERSPEECH"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2134087"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7953076"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2009.5372889"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2009.5372931"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178970"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2013.6707765"},{"key":"ref28","article-title":"Keras","author":"chollet","year":"2015"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2017.7953159"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2015.7404803"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2016-1460"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143891"},{"key":"ref29","article-title":"Theano: A Python framework for fast computation of mathematical\n expressions","year":"2016"},{"key":"ref5","volume":"247","author":"bourlard","year":"2012","journal-title":"Connectionist Speech Recognition A Hybrid Approach"},{"key":"ref8","first-page":"743","article-title":"Forward-backward retraining of recurrent neural networks","author":"senior","year":"0","journal-title":"Adv Neural Inform Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ASRU.2015.7404790"},{"key":"ref2","first-page":"605","article-title":"Empirical evaluation and combination of advanced\n language modeling techniques","author":"mikolov","year":"0","journal-title":"Proc INTERSPEECH"},{"key":"ref9","article-title":"Neural\n machine translation by jointly learning to align and translate","author":"bahdanau","year":"0","journal-title":"Proc ICLR"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2016-82"},{"key":"ref22","article-title":"Multi-view recurrent\n neural acoustic word embeddings","author":"he","year":"0","journal-title":"Proc ICLR"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472619"},{"key":"ref24","article-title":"Empirical evaluation of gated recurrent\n neural networks on sequence modeling","author":"chung","year":"0","journal-title":"NIPS Deep Learning Workshop"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2016-968"},{"key":"ref26","first-page":"2741","article-title":"Character-aware neural language models","author":"kim","year":"0","journal-title":"Proc AAAI"},{"key":"ref25","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc ICLR"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/ieeexplore.ieee.org\/ielaam\/4200690\/8103432\/8059818-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4200690\/8103432\/08059818.pdf?arnumber=8059818","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,8]],"date-time":"2022-04-08T18:52:11Z","timestamp":1649443931000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/8059818\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":32,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2017.2759726","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"value":"1932-4553","type":"print"},{"value":"1941-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12]]}}}