2015 SAI Intelligent Systems Conference (IntelliSys) 2015
DOI: 10.1109/intellisys.2015.7361150
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Unsupervised adaptation of ASR systems: An application of dynamic programming in machine learning

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Cited by 2 publications
(3 citation statements)
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“…In recent years, we see documentations on new Artificial Intelligence programing language like Logic and Objected-Oriented Programming Language (LOOP) documented by (Suciu et al, 2001) in 2001 which extended PROLOG logic programming language with object oriented features while (Ostermayer et al, 2014) discuss a connection architecture between PROLOG and JAVA. ARCHLOG documented in 2006 (Fidjeland and Luk, 2006) can produce high-performance designs without detailed knowledge of hardware development and a framework for designing multiprocessor architectures; Epistemic Ontology Language with Constraints (EOLC), which is used for specifying the epistemic ontology for heterogeneous verification was documented in 2007 by (Kumar and Krogh, 2007); McKinley described Python in his 2016 paper (McKinley, 2016a), (Ellis and Agah, 2012;Kurniawan et al, 2015;McKinley, 2016a;Park et al, 2017Park et al, ) in 2012Park et al, ,2015Park et al, ,2016Park et al, and 2017 respectively discussed the use of C++ in Artificial intelligence programming languages while (Babu et al, 2015;Garg and Kumar, 2017;Kurniawan et al, 2015;McKinley, 2016b;Mittal and Mandalika, 2015;Raff, 2017) discussed the use of JAVA in Artificial Intelligence programming languages.…”
Section: Study Characteristicsmentioning
confidence: 99%
See 1 more Smart Citation
“…In recent years, we see documentations on new Artificial Intelligence programing language like Logic and Objected-Oriented Programming Language (LOOP) documented by (Suciu et al, 2001) in 2001 which extended PROLOG logic programming language with object oriented features while (Ostermayer et al, 2014) discuss a connection architecture between PROLOG and JAVA. ARCHLOG documented in 2006 (Fidjeland and Luk, 2006) can produce high-performance designs without detailed knowledge of hardware development and a framework for designing multiprocessor architectures; Epistemic Ontology Language with Constraints (EOLC), which is used for specifying the epistemic ontology for heterogeneous verification was documented in 2007 by (Kumar and Krogh, 2007); McKinley described Python in his 2016 paper (McKinley, 2016a), (Ellis and Agah, 2012;Kurniawan et al, 2015;McKinley, 2016a;Park et al, 2017Park et al, ) in 2012Park et al, ,2015Park et al, ,2016Park et al, and 2017 respectively discussed the use of C++ in Artificial intelligence programming languages while (Babu et al, 2015;Garg and Kumar, 2017;Kurniawan et al, 2015;McKinley, 2016b;Mittal and Mandalika, 2015;Raff, 2017) discussed the use of JAVA in Artificial Intelligence programming languages.…”
Section: Study Characteristicsmentioning
confidence: 99%
“…Two studies in 1986 and 1987 [34][35] respectively dealt with different variants of Prolog namely TC-Prolog and FProlog. In recent years, we see documentations on new Artificial Intelligence programing language like Logic and Objected-oriented programming language (LOOP) documented by 36 in 2001 which extended Prolog logic programming language with object oriented features while 37 discuss a connection architecture between Prolog and JAVA, Archlog documented in 2006 38 which can produce high-performance designs without detailed knowledge of hardware development and a framework for designing multiprocessor architectures; Epistemic Ontology Language with Constraints (EOLC) which is used for specifying the epistemic ontology for heterogeneous verification, documented in 2007 by 39 ; McKinley described Python in his 2006 paper 40 , [40][41][42][43] in 2012,2015,2016 and 2017 respectively discussed the use of C++ in Artificial intelligence programming languages while 28,42,[44][45][46][47] discussed the use of JAVA in Artificial intelligence programming languages.…”
Section: Study Characteristicsmentioning
confidence: 99%
“…There have been many attempts to improve the recognition of accented speech, with varying degrees of success [7,8,9,10,11]. Some promising approaches include unsupervised adaptation [12,13], multitask learning with accent embeddings [14,15], and domain adversarial training [2,16]. While most approaches have delivered results, they either use massive amounts of accent data (e.g., 23K hours [2]), rely on corpora that are not publicly available [2,3], or use increasingly complex models [10,14,16] that do not shed light on how humans adapt so quickly to new accents.…”
Section: Related Workmentioning
confidence: 99%