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2018 | 52 | 2 |

Article title

Zarządzanie rozwojem systemów rozpoznawania mowy: problemy wydajności

Content

Title variants

Languages of publication

EN

Abstracts

EN
Speech recognition enables the transformation of spoken words and sentences into text in digital form. This technology is a subject of numerous studies and commercial development for many years. The aim of this paper is to examine performance issues of speech recognition and to manage the development in this field. Thorough analysis of performance limitations of speech recognition systems we identified main 11 issues to overcome. They indicate the direction of managing development of speech recognition systems.
PL
Rozpoznawanie mowy umożliwia przekształcanie wypowiadanych słów i zdań w tekst w formie cyfrowej. Technologia ta jest od wielu lat przedmiotem licznych badań naukowych oraz komercyjnych. Celem niniejszego artykułu jest zbadanie zagadnień dotyczących wydajności systemów rozpoznawania mowy i zarządzanie rozwojem tych systemów. Dogłębna analiza w zakresie ograniczeń wydajnościowych systemów rozpoznawania mowy pozwoliła na zidentyfikowanie problemów, które trzeba przezwyciężyć. Wskazują one kierunek zmian w zarządzaniu rozwojem systemów rozpoznawania mowy.

Year

Volume

52

Issue

2

Physical description

Dates

published
2018
online
2018-07-27

Contributors

References

  • Akbarinia A., Valdez Medrano J., Zamani R., Speech Recognition for Noisy Environments – Feasibility of Voice Command in Construction Settings, Engineer’s thesis, Department of Computer Science and Engineering, University of Gothenburg, Goteborg, 2011.
  • Anumanchipalli G.K., Oliveira L.C., Black A.W., Intent transfer in speech-to-speech machine translation, “IEEE Workshop on Spoken Language Technology” 2012, DOI: https://doi.org/10.1109/SLT.2012.6424214.
  • Anusuya M.A., Katti S.K., Speech Recognition by Machine: A Review, “International Journal of Computer Science and Information Security” 2009, Vol. 6(3).
  • Biadsy F., Automatic Dialect and Accent Recognition and its Application to Speech Recognition, Department of Computer Science, Columbia University, 2011 (doctoral dissertation).
  • Cloarec G., Jouvet D., Modeling inter-speaker variability in speech recognition, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2008, DOI: https://doi.org/10.1109/ICASSP.2008.4518663.
  • Gaikwad S., Gawali B., Yannawar P., A review on Speech Recognition Technique, “International Journal of Computer Applications” 2010, Vol. 10(3), DOI: https://doi.org/10.5120/1462-1976.
  • Gubka R., Kuba M., Jarina R., Universal approach for sequential audio pattern search, Proceedings of the 2013 Federated Conference on Computer Science and Information Systems FedCSIS, Annals of Computer Science and Information Systems, Kraków 2013.
  • Janicki A., Wawer D., Automatic Speech Recognition for Polish in a Computer Game Interface, Proceedings of the 2011 Federated Conference on Computer Science and Information Systems FedCSIS, Annals of Computer Science and Information Systems, Szczecin 2011.
  • Mary L., Extraction and Representation of Prosody for Speaker, Speech and Language Recognition, SpringerBriefs in Electrical and Computer Engineering, Springer, New York 2012, DOI: https://doi.org/10.1007/978-1-4614-1159-8.
  • Morgan N., Deep and Wide: Multiple Layers in Automatic Speech Recognition, “IEEE Transactions on Audio, Speech and Language Processing” 2012, Vol. 20(1), DOI: https://doi.org/10.1109/TASL.2011.2116010.
  • Nouza J., Zdansky J., Cerva P., Silovsky J., Challenges in Speech Processing of Slavic Languages (Case Studies in Speech Recognition of Czech and Slovak), [in:] A. Esposito, N. Campbell, C. Vogel, A. Hussain, A. Nijholt (eds.), Development of Multimodal Interfaces: Active Listening and Synchrony, Springer Verlag, Berlin–Heidelberg 2010.
  • Qin L., Learning Out-of-Vocabulary Words in Automatic Speech recognition, Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh 2013 (doctoral dissertation).
  • Seppi D., Demuynck K., Compernolle D. van, Template-based Automatic Speech Recognition meets prosody, 12th Annual Conference of the International Speech Communication Association (Interspeech 2011), Florence 2011.
  • Shanthi T., Chelpa L., Review of Feature Extraction Techniques in Automatic Speech Recognition, “International Journal of Scientific Engineering and Technology” 2013, Vol. 2(6).
  • Virtanen T., Singh R., Raj B. (eds.), Techniques for Noise Robustness in Automatic Speech Recognition, Wiley, London 2013.
  • Wu C.-H., Liu C.-H., Robust Speech Recognition for Adverse Environments, [in:] S. Ramakrishnan (ed.), Modern Speech Recognition Approaches with Case Studies, Intech 2012, DOI: https://doi.org/10.5772/47843.
  • Ziółko B., Ziółko M., Przetwarzanie mowy, Wydawnictwa AGH, Kraków 2011.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.ojs-doi-10_17951_h_2018_52_2_71-78
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