TY - BOOK AU - Minker,Wolfgang AU - Nakamura,Satoshi AU - Markov,Konstantin AU - Sakti,Sakriani ED - SpringerLink (Online service) TI - Incorporating Knowledge Sources into Statistical Speech Recognition T2 - Lecture Notes in Electrical Engineering, SN - 9780387858302 PY - 2009/// CY - Boston, MA PB - Springer US KW - ENGINEERING KW - COMPUTER COMMUNICATION NETWORKS KW - ACOUSTICS KW - COMPUTER ENGINEERING KW - TELECOMMUNICATION KW - ELECTRICAL ENGINEERING KW - COMMUNICATIONS ENGINEERING, NETWORKS KW - SIGNAL, IMAGE AND SPEECH PROCESSING N1 - and Book Overview -- Statistical Speech Recognition -- Graphical Framework to Incorporate Knowledge Sources -- Speech Recognition Using GFIKS -- Conclusions and Future Directions N2 - Incorporating Knowledge Sources into Statistical Speech Recognition offers solutions for enhancing the robustness of a statistical automatic speech recognition (ASR) system by incorporating various additional knowledge sources while keeping the training and recognition effort feasible. The authors provide an efficient general framework for incorporating knowledge sources into state-of-the-art statistical ASR systems. This framework, which is called GFIKS (graphical framework to incorporate additional knowledge sources), was designed by utilizing the concept of the Bayesian network (BN) framework. This framework allows probabilistic relationships among different information sources to be learned, various kinds of knowledge sources to be incorporated, and a probabilistic function of the model to be formulated. Incorporating Knowledge Sources into Statistical Speech Recognition demonstrates how the statistical speech recognition system may incorporate additional information sources by utilizing GFIKS at different levels of ASR. The incorporation of various knowledge sources, including background noises, accent, gender and wide phonetic knowledge information, in modeling is discussed theoretically and analyzed experimentally UR - http://dx.doi.org/10.1007/978-0-387-85830-2 ER -