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Additionally, we consider the biological plausibility of models in stepping away from exemplar input patterns towards a set of more general theoretical principles consistent with behavioral experiments.
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We introduce intermediate textures between multiple exemplars, called 'transition textures' which enable our algorithm to produce smooth transitions between input exemplar patches.
Selected source exemplars are replaced with target exemplars, and the input speech is converted.
This paper presents a new framework for the semi-automatic synthesis of an inhomogeneous texture from a theoretically infinite number of input exemplars.
We also propose 'image index correction' to achieve a synthesis speed that is independent of the number of input exemplars by smart placement of transition-texture samples.
That is, the proposal is an exemplar-based model that "recognizes input and generates outputs by analogical evaluation across a lexicon of distinct memory traces of remembered tokens of speech" (Gahl & Yu, 2006: 213; see also Bod, 2009).
This self-responsibility, r (B, B), is an evidence score that ranks whether gene B is an exemplar based on the input preferences in the procedure.
Figure 3 Schematic of the synthesis process: stored phoneme exemplars along with the input audio features are used to select optimal units to train a state-based manifold traversal model.
Using the joint audio-visual features as source features, VC performance is improved compared with that of a previous audio-input exemplar-based VC method.
The proposed multimodal VC technique was evaluated by comparing it with an exemplar-based audio-input method [5] and a conventional GMM-based method [6] in a speaker-conversion task using clean speech data and noise-added speech data.
Different from the traditional learning-based method, SmartTracing does not require human input of training exemplars and can self-adapt to different types of neuroimage data.
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