A Study on the Spoken Language Processing Technology Based on Big Data (빅데이터 기반 음성언어 처리 기술에 관한 연구)

Cho Jin-Kwan (조진관)

doi:10.34163/jkits.2019.14.4.008

A Study on the Spoken Language Processing Technology Based on Big Data

Journal of Knowledge Information Technology and Systems
Abbr : JKITS
2019, 14(4), pp.391-399
DOI : 10.34163/jkits.2019.14.4.008
Publisher : Korea Knowledge Information Technology Society
Research Area : Interdisciplinary Studies > Interdisciplinary Research
Received : June 6, 2019
Accepted : August 9, 2019
Published : August 31, 2019

Cho Jin-Kwan ¹

¹경동대학교

Accredited

ABSTRACT

Among the age of multiple information processing with smart device, many users feel each information and comfortable life through their smart device and computer so on. The area of language processing about multiple information make an automatically recognition and processing by computer with human language, and study their algorithm. In addition, it provides a several of application services such as siri, speech conversation system. Especially, language processing technology offer to valuable service in big data environment, and necessary to essential technology to processing big speech and text information. Moreover, big data-based application system become main resource that make a good performance using language processing technology by huge data. Moreover, our research provide web/app environment-based speech collaboration, big data-based both speech signal processing, and speech collaboration algorithm. Big data-based both speech signal processing and speech algorithm carry out by transaction procedure organically. By the transaction procedure, we had an experiment with Trek user speech data using RLS and SNR algorithm for big data-based language processing. Accordingly, in this paper, we propose big data-based information processing with many pre-research in order to improve a capability of the information processing technology using big data.

KEYWORDS

Information processing, Smart device, Language processing, Application service, Speech conversation system, Big data environment, Application system.

Citation status

* References for papers published after 2023 are currently being built.

[confproc] C. F. Baker / 1998 / The Berkeley FrameNet project / Proceedings of COLINGACL’98 : 86~90

[book] M. E. Beckman / 2006 / Prosodic Typology: The phonology of intonation and phrasing / Oxford University Press

[journal] R. A. Calvo / 2010 / Affect detection: An interdisciplinary review of models, methods, and their applications / IEEE Transactions On Affective Computing 1(1) : 18~37

[confproc] Y. Choi / 2008 / Learning with compositional semantics as structural inference for subsentential sentiment analysis / Proceedings of the Conference on Empirical Methods in Natural Language Processing : 793~801

[book] N. Chomsky / 2000 / Step by Step: Essays in Minimalist Syntax in Honor of Howard Lasnik / MIT Press : 89~115

[journal] M. Davel / 2008 / Pronunciation prediction with Default&Refine / Computer Speech and Language 22(4) : 374~393

[confproc] M. H. Davel / 2012 / Validating smartphone-collected speech corpora / Proceedings of the Third International Workshop on Spoken Languages Technologies for Under-Resourced Languages (SLTU’12)

[book] M. Dong / 2005 / Natural Language Processing – IJCNLP 2004, Vol. 3248 of Lecture Notes in Computer Science / : 272~279

[confproc] A. Esuli / 2006 / SentiWordNet: a publicly available lexical resource for opinion mining / Proceedings of the Fifth International Conference on Language Resources and Evaluation : 417~422

[book] C. F´ery / 2009 / The sound of Syntax / University Press : 271~290

[book] C. F´ery / 2009 / Information Structure from Different Perspectives / Oxford University Press : 36~63

[book] B. Fossett / 2009 / Handbook of Developmental disabilities, chapter augmentative and alternative communication / Guilford Press : 330~366

[book] D. J. Hirst / 2005 / Quantitative prosody modeling for natural speech description and generation, Vol. 46 of Speech Communication / : 334~347

[confproc] J. A. Louw / 2008 / Speect: A multilingual text-to-speech system / Proceedings of the 19th Annual Symposium of the Pattern Recognition Association of South Africa : 165~168

[book] B. Z. Pollermann / 2002 / Acoustic patterns of emotions / : 237~245

[confproc] D. Povey / 2011 / The Kaldi speech recognition toolkit / IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. IEEE Signal Processing Society : 123~130

[book] M. Schr¨oder / 2009 / Affective Information Processing / Springer London : 111~126

KJCKorea
Journal Central

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study on the Spoken Language Processing Technology Based on Big Data

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of Knowledge Information Technology and Systems 2023 KCI Impact Factor : 0.89

A Study on the Spoken Language Processing Technology Based on Big Data

ABSTRACT

KEYWORDS

Statistics

Tools

Issue List

Citation status

KCI Citation Counts (1)

REFERENCES (17) * References for papers published after 2023 are currently being built.

Search PDF

Citation

* References for papers published after 2023 are currently being built.