Incorporating Knowledge Sources into Statistical Speech Recognition

• Main Authors: Minker, Wolfgang. (Author), Nakamura, Satoshi. (Author), Markov, Konstantin. (Author), Sakti, Sakriani. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Boston, MA : Springer US, 2009.
• Series: Lecture Notes in Electrical Engineering, 42
• Subjects: Engineering.; Computer Communication Networks.; Acoustics.; Computer engineering.; Telecommunication.; Electrical Engineering.; Communications Engineering, Networks.; Signal, Image and Speech Processing.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-85830-2

 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.