Incorporating Knowledge Sources into Statistical Speech Recognition [electronic resource] / by Wolfgang Minker, Satoshi Nakamura, Konstantin Markov, Sakriani Sakti.
Contributor(s): Nakamura, Satoshi [author.] | Markov, Konstantin [author.] | Sakti, Sakriani [author.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Electrical Engineering: 42Publisher: Boston, MA : Springer US, 2009Description: online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387858302.Subject(s): Engineering | Computer Communication Networks | Acoustics | Computer engineering | Telecommunication | Engineering | Electrical Engineering | Computer Communication Networks | Communications Engineering, Networks | Acoustics | Signal, Image and Speech ProcessingOnline resources: Click here to access online
and Book Overview -- Statistical Speech Recognition -- Graphical Framework to Incorporate Knowledge Sources -- Speech Recognition Using GFIKS -- Conclusions and Future Directions.
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.