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N. P. Narendra - One of the best experts on this subject based on the ideXlab platform.
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Parameterization of Excitation Signal for Improving the Quality of HMM-Based Speech Synthesis System
Circuits Systems and Signal Processing, 2017Co-Authors: N. P. NarendraAbstract:This paper proposes a new approach of parameterizing the excitation signal for improving the quality of HMM-based speech synthesis system. The proposed method tries to model the excitation or residual signal by segregating the regions of the residual signal based on their perceptual importance. Initially, a study on the characteristics of the residual signal around glottal closure instant (GCI) is performed using principal Component analysis (PCA). Based on the present study, and from the previous literature (Adiga and Prasanna in Proceedings of Interspeech, pp 1677–1681, 2013; Cabral in Proceedings of Interspeech, pp 1082–1086, 2013), it is concluded that the segment of the residual signal around GCI which carries perceptually important information is considered as the Deterministic Component and the remaining part of the residual signal is considered as the noise Component. The Deterministic Component is compactly represented using PCA coefficients (with about 95% accuracy), and the noise Component is parameterized in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement of quality for both female and male speakers’ speech synthesized by the proposed method, compared to three existing excitation modeling methods. Accurate parameterization of the segment of the residual signal around GCI resulted in the improvement of the quality of the synthesized speech. Synthesized speech samples of the proposed and existing source models are made available online at http://www.sit.iitkgp.ernet.in/~ksrao/parametric-hts/pcd-hts.html.
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A Deterministic plus noise model of excitation signal using principal Component analysis for parametric speech synthesis
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: N. P. NarendraAbstract:This paper proposes a new approach of modeling the excitation signal as Deterministic and noise Components. Initially, a study on characteristics of excitation or residual signal around glottal closure instant (GCI) is performed using principal Component analysis (PCA). Based on the study, the segment of residual signal around GCI is considered as the Deterministic Component and the remaining part of the residual signal is considered as the noise Component. The Deterministic Component is parameterized using PCA coefficients, and the noise Component can be represented in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement in the quality of speech synthesized by the proposed method, compared to three existing methods.
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ICASSP - A Deterministic plus noise model of excitation signal using principal Component analysis for parametric speech synthesis
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: N. P. Narendra, K. Sreenivasa RaoAbstract:This paper proposes a new approach of modeling the excitation signal as Deterministic and noise Components. Initially, a study on characteristics of excitation or residual signal around glottal closure instant (GCI) is performed using principal Component analysis (PCA). Based on the study, the segment of residual signal around GCI is considered as the Deterministic Component and the remaining part of the residual signal is considered as the noise Component. The Deterministic Component is parameterized using PCA coefficients, and the noise Component can be represented in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement in the quality of speech synthesized by the proposed method, compared to three existing methods.
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Time-domain Deterministic plus noise model based hybrid source modeling for statistical parametric speech synthesis
Speech Communication, 2016Co-Authors: N. P. Narendra, K. Sreenivasa RaoAbstract:The source signal is modeled as a combination of Deterministic and noise Components.The estimated Deterministic Components are stored in the form of a decision tree.The noise Components are parameterized in terms of spectrum and amplitude envelopes.During synthesis, Deterministic Component is selected from the leaf of a decision tree.The noise Component is generated from the natural instance of the noise signal. This paper proposes a time-domain Deterministic plus noise model based hybrid source modeling framework for improving the quality of statistical parametric speech synthesis system. In the proposed approach, the excitation signal is modeled as a combination of Deterministic and noise Components. Time-domain pitch-synchronous analysis is performed on the excitation or residual signal. From the pitch-synchronous residual frames of a phone, the Deterministic and noise Components are estimated. The Deterministic Components of all phones are systematically arranged in the form of a decision tree. The spectrum and amplitude envelope of noise Components are modeled using hidden Markov models (HMMs). During synthesis, the suitable Deterministic Component is chosen from the leaf of a decision tree. The noise Component is obtained after imposing the target spectrum and amplitude envelopes generated from the HMMs. The sum of Deterministic and noise Components are pitch-synchronously overlap added to construct the excitation signal of a phone. The proposed hybrid source modeling approach is incorporated in the statistical parametric speech synthesis system. Performance evaluation results show that the proposed method is capable producing natural sounding synthetic speech and the quality is clearly better than the state-of-the-art statistical parametric speech synthesis systems. Synthesized speech samples of the proposed and the state-of-the-art methods used for the comparison are made available online at http://www.sit.iitkgp.ernet.in/~ksrao/HSM-SPSS/hsm.html.
Leopoldo Altamirano-robles - One of the best experts on this subject based on the ideXlab platform.
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CIARP - Deterministic Component of 2-D wold decomposition for geometry and texture descriptors discovery
Lecture Notes in Computer Science, 2007Co-Authors: E. D. López-espinoza, Leopoldo Altamirano-roblesAbstract:In this paper, the Deterministic Component of 2-D Wold decomposition is used to obtain texture descriptors in industrial plastic quality images, and hidden geometry of tree crown in remote sensing images. The texture image is decomposed into two texture images: a non-Deterministic texture and a Deterministic one. In order to obtain texture descriptors, a set of discriminant texture features is selected from the Deterministic Component. The texture descriptors have been used to distinguish among three kinds of plastic quality. The obtained texture descriptors are compared against texture descriptors obtained from the original image. With the objective to find hidden geometry of tree crown in remote sensing images, the Deterministic Component of the original image is analyzed. The observed geometry is compared against the modeled geometry in the literature of marked point processes.
K. Sreenivasa Rao - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - A Deterministic plus noise model of excitation signal using principal Component analysis for parametric speech synthesis
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: N. P. Narendra, K. Sreenivasa RaoAbstract:This paper proposes a new approach of modeling the excitation signal as Deterministic and noise Components. Initially, a study on characteristics of excitation or residual signal around glottal closure instant (GCI) is performed using principal Component analysis (PCA). Based on the study, the segment of residual signal around GCI is considered as the Deterministic Component and the remaining part of the residual signal is considered as the noise Component. The Deterministic Component is parameterized using PCA coefficients, and the noise Component can be represented in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement in the quality of speech synthesized by the proposed method, compared to three existing methods.
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Time-domain Deterministic plus noise model based hybrid source modeling for statistical parametric speech synthesis
Speech Communication, 2016Co-Authors: N. P. Narendra, K. Sreenivasa RaoAbstract:The source signal is modeled as a combination of Deterministic and noise Components.The estimated Deterministic Components are stored in the form of a decision tree.The noise Components are parameterized in terms of spectrum and amplitude envelopes.During synthesis, Deterministic Component is selected from the leaf of a decision tree.The noise Component is generated from the natural instance of the noise signal. This paper proposes a time-domain Deterministic plus noise model based hybrid source modeling framework for improving the quality of statistical parametric speech synthesis system. In the proposed approach, the excitation signal is modeled as a combination of Deterministic and noise Components. Time-domain pitch-synchronous analysis is performed on the excitation or residual signal. From the pitch-synchronous residual frames of a phone, the Deterministic and noise Components are estimated. The Deterministic Components of all phones are systematically arranged in the form of a decision tree. The spectrum and amplitude envelope of noise Components are modeled using hidden Markov models (HMMs). During synthesis, the suitable Deterministic Component is chosen from the leaf of a decision tree. The noise Component is obtained after imposing the target spectrum and amplitude envelopes generated from the HMMs. The sum of Deterministic and noise Components are pitch-synchronously overlap added to construct the excitation signal of a phone. The proposed hybrid source modeling approach is incorporated in the statistical parametric speech synthesis system. Performance evaluation results show that the proposed method is capable producing natural sounding synthetic speech and the quality is clearly better than the state-of-the-art statistical parametric speech synthesis systems. Synthesized speech samples of the proposed and the state-of-the-art methods used for the comparison are made available online at http://www.sit.iitkgp.ernet.in/~ksrao/HSM-SPSS/hsm.html.
Peter J Beek - One of the best experts on this subject based on the ideXlab platform.
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stochastic order parameter equation of isometric force production revealed by drift diffusion estimates
Physical Review E, 2006Co-Authors: T. D. Frank, R Friedrich, Peter J BeekAbstract:We address two questions that are central to understanding human motor control variability: what kind of dynamical Components contribute to motor control variability (i.e., Deterministic and/or random ones), and how are those Components structured? To this end, we derive a stochastic order parameter equation for isometric force production from experimental data using drift-diffusion estimates. We show that the force variability increases with the required force output because of a decrease of Deterministic stability and an accompanying increase of noise intensity. A structural analysis reveals that the Deterministic Component consists of a linear control loop, while the random Component involves a noise source that scales with force output. In addition, we present evidence for the existence of a subject-independent overall noise level of human isometric force production. © 2006 The American Physical Society.
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stochastic order parameter equation of isometric force production revealed by drift diffusion estimates
Physical Review E, 2006Co-Authors: T. D. Frank, R Friedrich, Peter J BeekAbstract:We address two questions that are central to understanding human motor control variability: what kind of dynamical Components contribute to motor control variability (i.e., Deterministic and/or random ones), and how are those Components structured? To this end, we derive a stochastic order parameter equation for isometric force production from experimental data using drift-diffusion estimates. We show that the force variability increases with the required force output because of a decrease of Deterministic stability and an accompanying increase of noise intensity. A structural analysis reveals that the Deterministic Component consists of a linear control loop, while the random Component involves a noise source that scales with force output. In addition, we present evidence for the existence of a subject-independent overall noise level of human isometric force production.
A. M. Robert Taylor - One of the best experts on this subject based on the ideXlab platform.
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Bootstrap Determination of the Co‐Integration Rank in VAR Models with Unrestricted Deterministic Components
Journal of Time Series Analysis, 2015Co-Authors: Giuseppe Cavaliere, Anders Rahbek, A. M. Robert TaylorAbstract:In a recent paper, Cavaliere et al., [Cavaliere G, 2012] develop bootstrap implementations of the popular likelihood‐based co‐integration rank tests and associated sequential rank determination procedures of Johansen [Johansen S, 1996]. By using estimates of the parameters of the underlying co‐integrated VAR model obtained under the restriction of the null hypothesis, they show that consistent bootstrap inference can be obtained for processes whose Deterministic Component is either zero, a restricted constant or a restricted trend. In this article, we extend their bootstrap approach to allow the Deterministic Component to follow the practically relevant cases of either an unrestricted constant or an unrestricted trend from Johansen [Johansen S, 1996]. A full asymptotic theory is provided for these two cases, establishing the asymptotic validity of the resulting bootstrap tests. Our results, taken together with those in Cavaliere et al., [Cavaliere G, 2012], therefore show that the bootstrap approach based on imposing the reduced rank null hypothesis is valid for all five of these Deterministic settings. Monte Carlo evidence demonstrates the improvements that the proposed bootstrap methods can deliver over the corresponding asymptotic procedures.
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The Flexible Fourier Form and Local GLS De-trended Unit Root Tests
2009Co-Authors: Paulo M.m. Rodrigues, A. M. Robert TaylorAbstract:In two recent papers Enders and Lee (2008) and Becker et al. (2006) provide Lagrange multiplier and OLS de-trended unit root tests, and stationarity tests, respectively, which incorporate a Fourier approximation element in the Deterministic Component. Such an approach can prove useful in providing robustness against a variety of breaks in the Deterministic trend function of unknown form and number. In this paper, we generalise the unit root testing procedure based on local GLS de-trending proposed by Elliott, Rothenberg and Stock (1996) to allow for a Fourier approximation to the unknown Deterministic Component in the same way. We show that although the resulting unit root tests possess good finite sample size and power properties, their limit null distributions are undefined.