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First, statistical synthesis models are generated for a target speaker using a speaker-dependent training algorithm.
Relations between the statistical synthesis and optimal decomposition of IMS projects are analysed.
AB quality preference tests were done to compare the performance of the hybrid SSCC algorithm with the statistical synthesis system.
Based on the effect size collected from each selected study, the meta-analysis performed the statistical synthesis of the data and produced a single summary effect of which the statistical significance was also assessed [34].
However, data on Foraminifera have mainly originated from geological studies that employ varied methodologies (e.g., different sieve fractions, depth intervals, wet vs. dry sorting, dead vs. live assemblages), which often hamper a thorough statistical synthesis of the data.
High levels of data heterogeneity negated statistical synthesis.
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He looked at hundreds of papers and forty-four meta-analyses (that is, statistical syntheses of related studies), and discovered a consistent decline effect over time, as many of the theories seemed to fade into irrelevance.
The proposed hybrid parameter generation algorithm is described in Section 6. Experimental results of the SSS and hybrid systems are reported and discussed in Section 7. Finally, a conclusion is done in Section 8. Statistical speech synthesis systems use parametric vocoders for synthesis.
The statistical speech synthesis (SSS) approach is a promising alternative synthesis technique.
Statistical speech synthesis (SSS) is a fast-growing research area for text-to-speech (TTS) systems.
Moreover, newer methodologies [48] optimize the existing ones or propose novel approaches such as expressivity-based selection of units, unit selection, and signal modification, as well as statistical parametric synthesis based on Hidden Markov Models [55].
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