Reducing over-smoothness in HMM-based speech synthesis using exemplar-based voice conversion
نویسندگان
چکیده
منابع مشابه
Reducing over-smoothness in HMM-based speech synthesis using exemplar-based voice conversion
Speech synthesis has been applied in many kinds of practical applications. Currently, state-of-the-art speech synthesis uses statistical methods based on hidden Markov model (HMM). Speech synthesized by statistical methods can be considered over-smooth caused by the averaging in statistical processing. In the literature, there have been many studies attempting to solve over-smoothness in speech...
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In this paper, we describe an approach to voice characteristics conversion for an HMM-based text-to-speech synthesis system. Since this speech synthesis system uses phoneme HMMs as speech units, voice characteristics conversion is achieved by changing HMM parameters appropriately. To transform the voice characteristics of synthesized speech to the target speaker, we applied MAP/VFS algorithm to...
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Although Hidden Markov Model based speech synthesis has been proved to have good performance, there are still some factors which degrade the quality of synthesized speech: vocoder, model accuracy and over-smoothing. This paper analyzes these factors separately. Modifications for removing different factors are proposed. Experimental results show that over-smoothing in frequency domain mainly aff...
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SUMMARY This paper presents a voice conversion (VC) technique for noisy environments, where parallel exemplars are introduced to encode the source speech signal and synthesize the target speech signal. The parallel exemplars (dictionary) consist of the source exemplars and target exem-plars, having the same texts uttered by the source and target speakers. The input source signal is decomposed i...
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In the traditional voice conversion, converted speech is generated using statistical parametric models (for example Gaussian mixture model) whose parameters are estimated from parallel training utterances. A well-known problem of the statistical parametric methods is that statistical average in parameter estimation results in the over-smoothing of the speech parameter trajectories, and thus lea...
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ژورنال
عنوان ژورنال: EURASIP Journal on Audio, Speech, and Music Processing
سال: 2017
ISSN: 1687-4722
DOI: 10.1186/s13636-017-0113-5