An adaptive algorithm for mel-cepstral analysis of speech
T. Fukada,Keiichi Tokuda,Takao Kobayashi,Satoshi Imai +3 more
- Vol. 1, pp 137-140
TLDR
The authors apply the criterion used in the unbiased estimation of log spectrum to the spectral model represented by the mel-cepstral coefficients to solve the nonlinear minimization problem involved in the method and derive an adaptive algorithm whose convergence is guaranteed.Abstract:
The authors describe a mel-cepstral analysis method and its adaptive algorithm. In the proposed method, the authors apply the criterion used in the unbiased estimation of log spectrum to the spectral model represented by the mel-cepstral coefficients. To solve the nonlinear minimization problem involved in the method, they give an iterative algorithm whose convergence is guaranteed. Furthermore, they derive an adaptive algorithm for the mel-cepstral analysis by introducing an instantaneous estimate for gradient of the criterion. The adaptive mel-cepstral analysis system is implemented with an IIR adaptive filter which has an exponential transfer function, and whose stability is guaranteed. The authors also present examples of speech analysis and results of an isolated word recognition experiment. >read more
Citations
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Journal ArticleDOI
Statistical Parametric Speech Synthesis
TL;DR: This paper gives a general overview of techniques in statistical parametric speech synthesis, and contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years.
Proceedings ArticleDOI
Speech parameter generation algorithms for HMM-based speech synthesis
TL;DR: A speech parameter generation algorithm for HMM-based speech synthesis, in which the speech parameter sequence is generated from HMMs whose observation vector consists of a spectral parameter vector and its dynamic feature vectors, is derived.
Proceedings ArticleDOI
Statistical parametric speech synthesis using deep neural networks
TL;DR: This paper examines an alternative scheme that is based on a deep neural network (DNN), the relationship between input texts and their acoustic realizations is modeled by a DNN, and experimental results show that the DNN- based systems outperformed the HMM-based systems with similar numbers of parameters.
Proceedings Article
Simultaneous Modeling of Spectrum, Pitch and Duration in HMM-Based Speech Synthesis
TL;DR: An HMM-based speech synthesis system in which spectrum, pitch and state duration are modeled simultaneously in a unified framework of HMM is described.
The HMM-based speech synthesis system (HTS) version 2.0.
Heiga Zen,Takashi Nose,Junichi Yamagishi,Shinji Sako,Takashi Masuko,Alan W. Black,Keiichi Tokuda +6 more
TL;DR: This paper describes HTS version 2.0 in detail, as well as future release plans, which include a number of new features which are useful for both speech synthesis researchers and developers.
References
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Book
Adaptive Signal Processing
Bernard Widrow,Samuel D. Stearns +1 more
TL;DR: This chapter discusses Adaptive Arrays and Adaptive Beamforming, as well as other Adaptive Algorithms and Structures, and discusses the Z-Transform in Adaptive Signal Processing.
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Mel Log Spectrum Approximation (MLSA) filter for speech synthesis
TL;DR: A method of constructing the mel-log spectrum approximation (MLSA) filter, which has a relatively simple structure and a low coefficient sensitivity, together with a design example of MLSA filter for speech synthesis.
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Efficient Algorithm for Toeplitz Plus Hankel Matrices
Israel Gohberg,I. Koltracht +1 more
Proceedings ArticleDOI
Complex Chebyshev approximation for IIR digital filters using an iterative WLS technique
Takao Kobayashi,Satoshi Imai +1 more
TL;DR: A technique for designing IIR (infinite impulse response) filters with a complex-valued frequency response is proposed, whose real and imaginary parts represent the log magnitude and phase errors, respectively, is minimized using an iterative weighted-least-squares technique in the frequency domain.
Proceedings ArticleDOI
Adaptive filtering based on cepstral representation-adaptive cepstral analysis of speech
TL;DR: It is shown that the adaptive cepstral analysis method based on an unbiased estimation of the log spectrum has fast convergence properties in comparison with the least-mean-square algorithm.