The Experts below are selected from a list of 139215 Experts worldwide ranked by ideXlab platform

F. Nakamura - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP (1) - Automatic determination of acoustic Model Topology using variational Bayesian estimation and clustering
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: T. Watanabe, L. Sako, F. Nakamura
    Abstract:

    We describe the automatic determination of an acoustic Model for speech recognition, which is very complicated and includes latent variables, using VBEC: variational Bayesian estimation and clustering for speech recognition. We propose an efficient Gaussian mixture Model (GMM) based phonetic decision tree construction within the VBEC framework. The proposed method features a novel approach to reduce the unrealistically large number of computations needed for iterative calculations in the GMM-based decision tree method to a practical level by assuming that each Gaussian per state has the same occupancy and is represented by the same posterior distribution for the covariance parameter. The experimental results confirmed that VBEC automatically provided an optimum Model Topology with the highest performance level.

Atsushi Nakamura - One of the best experts on this subject based on the ideXlab platform.

  • Automatic determination of acoustic Model Topology using variational Bayesian estimation and clustering for large vocabulary continuous speech recognition
    IEEE Transactions on Audio Speech and Language Processing, 2006
    Co-Authors: Shinji Watanabe, Atsushi Sako, Atsushi Nakamura
    Abstract:

    We describe the automatic determination of a large and complicated acoustic Model for speech recognition by using variational Bayesian estimation and clustering (VBEC) for speech recognition. We propose an efficient method for decision tree clustering based on a Gaussian mixture Model (GMM) and an efficient Model search algorithm for finding an appropriate acoustic Model Topology within the VBEC framework. GMM-based decision tree clustering for triphone HMM states features a novel approach designed to reduce the overly large number of computations to a practical level by utilizing the statistics of monophone hidden Markov Model states. The Model search algorithm also reduces the search space by utilizing the characteristics of the acoustic Model. The experimental results confirmed that VBEC automatically and rapidly yielded an optimum Model Topology with the highest performance.

Sun Limin - One of the best experts on this subject based on the ideXlab platform.

  • Development Status of Wireless Sensor Network
    ZTE communications, 2009
    Co-Authors: Sun Limin
    Abstract:

    The article introduces the startup,roadmap of Wireless Sensor Network(WSN),and its maturity in techniques and market,and surveys key research topics and techniques supporting applications in this area,including networking Model,Topology control,media access and link control,routing,data forwarding and cross-layer design technique,time synchronization,node positioning,object tracking,etc.Based on practical application and demonstration of the typical systems,the paper classifies applications of WSN.

Hans Lehrach - One of the best experts on this subject based on the ideXlab platform.

  • Resources, standards and tools for systems biology.
    Briefings in functional genomics & proteomics, 2007
    Co-Authors: Christoph Wierling, Ralf Herwig, Hans Lehrach
    Abstract:

    Modelling and simulation techniques are valuable tools for the understanding of complex biological systems. The design of a computer Model necessarily has many diverse inputs, such as information on the Model Topology, reaction kinetics and experimental data, derived either from the literature, databases or direct experimental investigation. In this review, we describe different data resources, standards and Modelling and simulation tools that are relevant to integrative systems biology.

T. Watanabe - One of the best experts on this subject based on the ideXlab platform.

  • ICASSP (1) - Automatic determination of acoustic Model Topology using variational Bayesian estimation and clustering
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: T. Watanabe, L. Sako, F. Nakamura
    Abstract:

    We describe the automatic determination of an acoustic Model for speech recognition, which is very complicated and includes latent variables, using VBEC: variational Bayesian estimation and clustering for speech recognition. We propose an efficient Gaussian mixture Model (GMM) based phonetic decision tree construction within the VBEC framework. The proposed method features a novel approach to reduce the unrealistically large number of computations needed for iterative calculations in the GMM-based decision tree method to a practical level by assuming that each Gaussian per state has the same occupancy and is represented by the same posterior distribution for the covariance parameter. The experimental results confirmed that VBEC automatically provided an optimum Model Topology with the highest performance level.