The Experts below are selected from a list of 288015 Experts worldwide ranked by ideXlab platform
Tadashi Kitamura - One of the best experts on this subject based on the ideXlab platform.
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a hidden semi Markov Model based speech synthesis system
IEICE Transactions on Information and Systems, 2007Co-Authors: Keiichi Tokuda, Takao Kobayasih, Takashi Masuko, Tadashi KitamuraAbstract:A statistical speech synthesis system based on the hidden Markov Model (HMM) was recently proposed. In this system, spectrum, excitation, and duration of speech are Modeled simultaneously by context-dependent HMMs, and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative Model framework and solves it based on the maximum likelihood (ML) criterion. However, there is an inconsistency: although state duration probability density functions (PDFs) are explicitly used in the synthesis part of the system, they have not been incorporated into its training part. This inconsistency can make the synthesized speech sound less natural. In this paper, we propose a statistical speech synthesis system based on a hidden semi-Markov Model (HSMM), which can be viewed as an HMM with explicit state duration PDFs. The use of HSMMs can solve the above inconsistency because we can incorporate the state duration PDFs explicitly into both the synthesis and the training parts of the system. Subjective listening test results show that use of HSMMs improves the reported naturalness of synthesized speech.
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hidden semi Markov Model based speech synthesis
Conference of the International Speech Communication Association, 2004Co-Authors: Keiichi Tokuda, Takashi Masuko, Takao Kobayashi, Tadashi KitamuraAbstract:In the present paper, a hidden-semi Markov Model (HSMM) based speech synthesis system is proposed. In a hidden Markov Model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions Modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text and determine state durations maximizing their probabilities, then a speech parameter vector sequence is generated for the given state sequence. However, there is an inconsistency: although the speech is synthesized from HMMs with explicit state duration probability distributions, HMMs are trained without them. In the present paper, we introduce an HSMM, which is an HMM with explicit state duration probability distributions, into the HMM-based speech synthesis system. Experimental results show that the use of HSMM training improves the naturalness of the synthesized speech.
Keiichi Tokuda - One of the best experts on this subject based on the ideXlab platform.
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a hidden semi Markov Model based speech synthesis system
IEICE Transactions on Information and Systems, 2007Co-Authors: Keiichi Tokuda, Takao Kobayasih, Takashi Masuko, Tadashi KitamuraAbstract:A statistical speech synthesis system based on the hidden Markov Model (HMM) was recently proposed. In this system, spectrum, excitation, and duration of speech are Modeled simultaneously by context-dependent HMMs, and speech parameter vector sequences are generated from the HMMs themselves. This system defines a speech synthesis problem in a generative Model framework and solves it based on the maximum likelihood (ML) criterion. However, there is an inconsistency: although state duration probability density functions (PDFs) are explicitly used in the synthesis part of the system, they have not been incorporated into its training part. This inconsistency can make the synthesized speech sound less natural. In this paper, we propose a statistical speech synthesis system based on a hidden semi-Markov Model (HSMM), which can be viewed as an HMM with explicit state duration PDFs. The use of HSMMs can solve the above inconsistency because we can incorporate the state duration PDFs explicitly into both the synthesis and the training parts of the system. Subjective listening test results show that use of HSMMs improves the reported naturalness of synthesized speech.
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hidden semi Markov Model based speech synthesis
Conference of the International Speech Communication Association, 2004Co-Authors: Keiichi Tokuda, Takashi Masuko, Takao Kobayashi, Tadashi KitamuraAbstract:In the present paper, a hidden-semi Markov Model (HSMM) based speech synthesis system is proposed. In a hidden Markov Model (HMM) based speech synthesis system which we have proposed, rhythm and tempo are controlled by state duration probability distributions Modeled by single Gaussian distributions. To synthesis speech, it constructs a sentence HMM corresponding to an arbitralily given text and determine state durations maximizing their probabilities, then a speech parameter vector sequence is generated for the given state sequence. However, there is an inconsistency: although the speech is synthesized from HMMs with explicit state duration probability distributions, HMMs are trained without them. In the present paper, we introduce an HSMM, which is an HMM with explicit state duration probability distributions, into the HMM-based speech synthesis system. Experimental results show that the use of HSMM training improves the naturalness of the synthesized speech.
Samer Mohammed - One of the best experts on this subject based on the ideXlab platform.
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automatic recognition of gait phases using a multiple regression hidden Markov Model
IEEE-ASME Transactions on Mechatronics, 2018Co-Authors: Ferhat Attal, Yacine Amirat, Abdelghani Chibani, Samer MohammedAbstract:This paper presents a new approach for automatic recognition of gait phases based on the use of an in-shoe pressure measurement system and a multiple-regression hidden Markov Model (MRHMM) that takes into account the sequential completion of the gait phases. Recognition of gait phases is formulated as a multiple polynomial regression problem, in which each phase, called a segment, is Modeled using an appropriate polynomial function. The MRHMM is learned in an unsupervised manner to avoid manual data labeling, which is a laborious time-consuming task that is subject to potential errors, particularly for large amounts of data. To evaluate the efficiency of the proposed approach, several performance metrics for classification are used: accuracy, F-measure, recall, and precision. Experiments conducted with five subjects during walking show the potential of the proposed method to recognize gait phases with relatively high accuracy. The proposed approach outperforms standard unsupervised classification methods (Gaussian mixture Model, k-means, and hidden Markov Model), while remaining competitive with respect to standard supervised classification methods (support vector machine, random forest, and k-nearest neighbor).
Xing Wang - One of the best experts on this subject based on the ideXlab platform.
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a dynamic multi scale Markov Model based methodology for remaining life prediction
Mechanical Systems and Signal Processing, 2011Co-Authors: Jihong Yan, Chaozhong Guo, Xing WangAbstract:The ability to accurately predict the remaining life of partially degraded components is crucial in prognostics. In this paper, a performance degradation index is designed using multi-feature fusion techniques to represent deterioration severities of facilities. Based on this indicator, an improved Markov Model is proposed for remaining life prediction. Fuzzy C-Means (FCM) algorithm is employed to perform state division for Markov Model in order to avoid the uncertainty of state division caused by the hard division approach. Considering the influence of both historical and real time data, a dynamic prediction method is introduced into Markov Model by a weighted coefficient. Multi-scale theory is employed to solve the state division problem of multi-sample prediction. Consequently, a dynamic multi-scale Markov Model is constructed. An experiment is designed based on a Bently-RK4 rotor testbed to validate the dynamic multi-scale Markov Model, experimental results illustrate the effectiveness of the methodology.
Shigeki Sagayama - One of the best experts on this subject based on the ideXlab platform.
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multiple regression hidden Markov Model
International Conference on Acoustics Speech and Signal Processing, 2001Co-Authors: K Fujinaga, Mitsuru Nakai, Hiroshi Shimodaira, Shigeki SagayamaAbstract:Proposes a class of hidden Markov Model (HMM) called multiple-regression HMM (MR-HMM) that utilizes auxiliary features such as fundamental frequency (F/sub 0/) and speaking styles that affect spectral parameters to better Model the acoustic features of phonemes. Though such auxiliary features are considered to be the factors that degrade the performance of speech recognizers, the proposed MR-HMM adapts its Model parameters, i.e. mean vectors of output probability distributions, depending on these auxiliary information to improve the recognition accuracy. Formulation for parameter reestimation of MR-HMM based on the EM algorithm is given in the paper. Experiments of speaker-dependent isolated word recognition demonstrated that MR-HMMs using F/sub 0/ based auxiliary features reduced the error rates by more than 20% compared with the conventional HMMs.
