A DCOM-based Turkish speech recognition system: TREN – turkish recognition ENgine

  • Authors:

  • Hasan Palaz;Alper Kanak;Yücel Bicil;Mehmet Ugur Dogan

  • Affiliations:

  • The Scientific and Technical Research Council of Turkey-National Research Institute of Electronics and Cryptology, TÜBİTAK-UEKAE, Gebze, Kocaeli, Turkey;The Scientific and Technical Research Council of Turkey-National Research Institute of Electronics and Cryptology, TÜBİTAK-UEKAE, Gebze, Kocaeli, Turkey;The Scientific and Technical Research Council of Turkey-National Research Institute of Electronics and Cryptology, TÜBİTAK-UEKAE, Gebze, Kocaeli, Turkey;The Scientific and Technical Research Council of Turkey-National Research Institute of Electronics and Cryptology, TÜBİTAK-UEKAE, Gebze, Kocaeli, Turkey

  • Venue:
  • ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Turkish Recognition ENgine (TREN) is a modular, Hidden Markov Model based (HMM-based), speaker independent and Distributed Component Object Model based (DCOM-based) speech recognition system. TREN contains specialized modules that allow a fully interoperable platform including a Turkish speech recognizer, a feature extractor, an end-point detector and a performance monitoring module. TREN deals with the interaction between two layers constituting the distributed architecture of TREN. The first layer is the central server, which applies some speech signal preprocessing and distributes the recognition calls to the appropriate remote servers according to their current CPU load of the recognition process. The second layer is composed of the remote servers performing the critical recognition task. In order to increase the recognition performance, a Turkish telephony speech database with a very large word corpus is collected and statistically the widest span of triphones representing Turkish is examined. TREN has been used to assist speech technologies which require a modular and multithreaded recognizer with dynamic load sharing facilities.