Speech parameter generation from HMM using dynamic features

Tokuda, Keiichi; Kobayashi, Takao; Imai, Shoichi

doi:10.1109/icassp.1995.479684

Cited by 205 publications

(124 citation statements)

References 4 publications

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“…As a result, the distance between the mean vectors of adjacent states can be large. Even the parameter generation algorithm proposed by [26][27][28] cannot compensate such jumps. In such cases, the quality of synthetic speech with HSMM is expected to deteriorate.…”

Section: Illustratory Examplementioning

confidence: 99%

“…These concepts involve Viterbi algorithm, methods which calculate forward/backward variables and occupation probabilities, and even all parameter generation algorithms [26][27][28]. It just needs to define mean vectors, covariance matrices, and space probabilities of HSMM in accordance with Equation 20.…”

Section: Hmem Improves Both State Duration Distributionmentioning

confidence: 99%

“…However, these advantages are achieved at the expense of one major disadvantage, i.e., degradation in the quality of synthetic speech [1]. This shortcoming results from three important factors: vocoding distortion [10][11][12][13], accuracy of statistical models [14][15][16][17][18][19][20][21][22][23][24][25], and accuracy of parameter generation algorithms [26][27][28]. This paper is an attempt to alleviate the second factor and improve the accuracy of statistical models.…”

Section: Introductionmentioning

confidence: 99%

“…Next, the relationship between acoustic and contextual factors is modeled using a context-dependent statistical approach [14][15][16][17][18][19][20][21][22][23][24][25]. Synthesis phase starts with a parameter generation algorithm [26][27][28] that exploits trained context-dependent statistical models and aims to generate realistic acoustic feature trajectories for a given input text. Acoustic trajectories are then fed into the same vocoder used during the training phase in order to generate the desired synthesized speech.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Context-dependent acoustic modeling based on hidden maximum entropy model for statistical parametric speech synthesis

Khorram

Sameti

Bahmaninezhad

et al. 2014

J AUDIO SPEECH MUSIC PROC.

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Decision tree-clustered context-dependent hidden semi-Markov models (HSMMs) are typically used in statistical parametric speech synthesis to represent probability densities of acoustic features given contextual factors. This paper addresses three major limitations of this decision tree-based structure: (i) The decision tree structure lacks adequate context generalization. (ii) It is unable to express complex context dependencies. (iii) Parameters generated from this structure represent sudden transitions between adjacent states. In order to alleviate the above limitations, many former papers applied multiple decision trees with an additive assumption over those trees. Similarly, the current study uses multiple decision trees as well, but instead of the additive assumption, it is proposed to train the smoothest distribution by maximizing entropy measure. Obviously, increasing the smoothness of the distribution improves the context generalization. The proposed model, named hidden maximum entropy model (HMEM), estimates a distribution that maximizes entropy subject to multiple moment-based constraints. Due to the simultaneous use of multiple decision trees and maximum entropy measure, the three aforementioned issues are considerably alleviated. Relying on HMEM, a novel speech synthesis system has been developed with maximum likelihood (ML) parameter re-estimation as well as maximum output probability parameter generation. Additionally, an effective and fast algorithm that builds multiple decision trees in parallel is devised. Two sets of experiments have been conducted to evaluate the performance of the proposed system. In the first set of experiments, HMEM with some heuristic context clusters is implemented. This system outperformed the decision tree structure in small training databases (i.e., 50, 100, and 200 sentences). In the second set of experiments, the HMEM performance with four parallel decision trees is investigated using both subjective and objective tests. All evaluation results of the second experiment confirm significant improvement of the proposed system over the conventional HSMM.

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Section: Illustratory Examplementioning

confidence: 99%

Section: Hmem Improves Both State Duration Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Context-dependent acoustic modeling based on hidden maximum entropy model for statistical parametric speech synthesis

Khorram

Sameti

Bahmaninezhad

et al. 2014

J AUDIO SPEECH MUSIC PROC.

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show abstract

“…Finally, the means and variances of the output acoustic parameter vector are generated by the HMM model. In order to avoid the discontinuities that would arise from a maximum likelihood approach, the acoustic parameter sequence is smoothed with the introduction of dynamic features and the use of the maximum likelihood parameter generation (MLPG) algorithm [24].…”

Section: Hmm-basedmentioning

confidence: 99%

Statistical Text-to-Speech Synthesis of Spanish Subtitles

Piqueras

Del-Agua

Giménez

et al. 2014

Advances in Speech and Language Technologies for Iberian Languages

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Abstract. Online multimedia repositories are growing rapidly. However, language barriers are often difficult to overcome for many of the current and potential users. In this paper we describe a TTS Spanish system and we apply it to the synthesis of transcribed and translated video lectures. A statistical parametric speech synthesis system, in which the acoustic mapping is performed with either HMM-based or DNN-based acoustic models, has been developed. To the best of our knowledge, this is the first time that a DNN-based TTS system has been implemented for the synthesis of Spanish. A comparative objective evaluation between both models has been carried out. Our results show that DNN-based systems can reconstruct speech waveforms more accurately.

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Speaker adaptation of pitch and spectrum for HMM‐based speech synthesis

Tamura

Masuko

Tokuda

et al. 2004

Systems & Computers in Japan

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SUMMARYThis paper describes a technique for synthesizing speech with an arbitrary speaker's voice using speaker-independent speech units, which we call "average voice" models. The proposed method is based on an HMM-based text-to-speech synthesis system. In the HMM-based speech synthesis system, the spectrum and the pitch parameter are modeled simultaneously using an HMM based on the multi-space probability distribution (MSD). By appropriately transforming the HMM parameters, the voice characteristics and the prosodic features of synthesized speech can be transformed. In this paper, we derive an extension of the MLLR algorithm to apply it to MSD-HMMs. It is shown that by speaker adaptation for the pitch and applying the spectrum model, not only the voice characteristics, but also the prosodic features, can be adapted by using a small number of sentences uttered by a target speaker. Through the subjective evaluation, it is shown that by applying simultaneous adaptation of pitch and spectrum, synthetic speech generated from adapted models from the average voice model using a small number of sentences is very close to that from speaker-dependent models.

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Speech parameter generation from HMM using dynamic features

Cited by 205 publications

References 4 publications

Context-dependent acoustic modeling based on hidden maximum entropy model for statistical parametric speech synthesis

Context-dependent acoustic modeling based on hidden maximum entropy model for statistical parametric speech synthesis

Statistical Text-to-Speech Synthesis of Spanish Subtitles

Speaker adaptation of pitch and spectrum for HMM‐based speech synthesis

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