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• W2395432258 abstract "This paper proposes to use KLD between context-dependent HMMs as target cost in unit selection TTS systems. We train context-dependent HMMs to characterize the contextual attributes of units, and calculate Kullback-Leibler Divergence (KLD) between the corresponding models. We demonstrate that the KLD measure provides a statistically meaningful way to analyze the underlining relations among elements of attributes. With the aid of multidimensional scaling, a set of attributes, including phonetic, prosodic and numerical contexts, are examined by graphically representing elements of the attribute as points on a low dimensional space, where the distances among points agree with the KLDs among the elements. The KLD between multi-space probability distribution HMMs is derived. A perceptual experiment shows that the TTT system defined with the KLD-based target cost sounds slightly better than one with the manuallytuned." @default.
• W2395432258 created "2016-06-24" @default.
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• W2395432258 date "2007-01-01" @default.
• W2395432258 modified "2023-09-24" @default.
• W2395432258 title "Measuring attribute dissimilarity with HMM KL-divergence for speech synthesis." @default.
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