The Experts below are selected from a list of 35250 Experts worldwide ranked by ideXlab platform

David K Hoffman - One of the best experts on this subject based on the ideXlab platform.

Hsiaochuan Wang - One of the best experts on this subject based on the ideXlab platform.

  • a segmental probabilistic model of speech using an orthogonal Polynomial Representation application to text independent speaker verification
    Speech Communication, 1996
    Co-Authors: Hsiaochuan Wang
    Abstract:

    Abstract A segmental probabilistic model based on an orthogonal Polynomial Representation of speech signals is proposed. Unlike the conventional frame based probabilistic model, this segment based model concatenates the similar acoustic characteristics of consecutive frames into an acoustic segment and represents the segment by an orthogonal Polynomial function. An iterative algorithm that performs recognition and segmentation processes is proposed for estimating the segment model. This segment model is applied in the text independent speaker verification. Tests were carried out on a 20-speaker database. With the best version of the model, an equal error rate of 0.59% can be reached, for test utterances of 10 digits. This corresponds to a relative error rate reduction of more than 50%, compared to the conventional frame based probabilistic model.

  • an orthogonal Polynomial Representation of speech signals and its probabilistic model for text independent speaker verification
    International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: Hsiaochuan Wang, F K Soong, Chaoshih Huang
    Abstract:

    A segmental probabilistic model based on an orthogonal Polynomial Representation of speech signals is proposed. Unlike the conventional frame based probabilistic model, this segment based model concatenates the similar acoustic characteristics of consecutive frames into an acoustic segment and represents the segment by an orthogonal Polynomial function. An algorithm which iteratively performs recognition and segmentation processes is proposed for estimating the parameters of the segment model. This segment model is applied in the text independent speaker verification. For a 20-speaker database, the experimental results show that the performance by using segment models is better than that by using the conventional frame based probabilistic model. The equal error rate can be reduced by 3.6% when the models are represented by 64-mixture density functions.

Dominika Zgid - One of the best experts on this subject based on the ideXlab platform.

  • chebyshev Polynomial Representation of imaginary time response functions
    Physical Review B, 2018
    Co-Authors: Emanuel Gull, Sergei Iskakov, Igor Krivenko, Alexander A Rusakov, Dominika Zgid
    Abstract:

    Problems of finite-temperature quantum statistical mechanics can be formulated in terms of imaginary (Euclidean) -time Green's functions and self-energies. In the context of realistic Hamiltonians, the large energy scale of the Hamiltonian (as compared to temperature) necessitates a very precise Representation of these functions. In this paper, we explore the Representation of Green's functions and self-energies in terms of a series of Chebyshev Polynomials. We show that many operations, including convolutions, Fourier transforms, and the solution of the Dyson equation, can straightforwardly be expressed in terms of the series expansion coefficients. We then compare the accuracy of the Chebyshev Representation for realistic systems with the uniform-power grid Representation, which is most commonly used in this context.

  • chebyshev Polynomial Representation of imaginary time response functions
    Physical Review B, 2018
    Co-Authors: Emanuel Gull, Sergei Iskakov, Igor Krivenko, Alexander A Rusakov, Dominika Zgid
    Abstract:

    Problems of finite-temperature quantum statistical mechanics can be formulated in terms of imaginary (Euclidean) -time Green's functions and self-energies. In the context of realistic Hamiltonians, the large energy scale of the Hamiltonian (as compared to temperature) necessitates a very precise Representation of these functions. In this paper, we explore the Representation of Green's functions and self-energies in terms of a series of Chebyshev Polynomials. We show that many operations, including convolutions, Fourier transforms, and the solution of the Dyson equation, can straightforwardly be expressed in terms of the series expansion coefficients. We then compare the accuracy of the Chebyshev Representation for realistic systems with the uniform-power grid Representation, which is most commonly used in this context.

Youhong Huang - One of the best experts on this subject based on the ideXlab platform.

Chaoshih Huang - One of the best experts on this subject based on the ideXlab platform.

  • an orthogonal Polynomial Representation of speech signals and its probabilistic model for text independent speaker verification
    International Conference on Acoustics Speech and Signal Processing, 1995
    Co-Authors: Hsiaochuan Wang, F K Soong, Chaoshih Huang
    Abstract:

    A segmental probabilistic model based on an orthogonal Polynomial Representation of speech signals is proposed. Unlike the conventional frame based probabilistic model, this segment based model concatenates the similar acoustic characteristics of consecutive frames into an acoustic segment and represents the segment by an orthogonal Polynomial function. An algorithm which iteratively performs recognition and segmentation processes is proposed for estimating the parameters of the segment model. This segment model is applied in the text independent speaker verification. For a 20-speaker database, the experimental results show that the performance by using segment models is better than that by using the conventional frame based probabilistic model. The equal error rate can be reduced by 3.6% when the models are represented by 64-mixture density functions.