Locally Linear Embedding for Exemplar-Based Spectral Conversion

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Neural Voice Cloning with a Few Samples (2018, methodology): “Wu et al. [2016] uses a spectral conversion approach integrated with the locally linear embeddings for manifold learning.”
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning (2020, background): “includes non-negative spectrogram deconvolution [145], locally linear embedding (LLE) [146], and unit selection [20].”
An overview of voice conversion systems (2017, methodology): “Several other extensions of NMF approaches have been proposed, such as mapping the activation matrix ( AIHARA et al., 2015 ), many-to-many VC ( Aihara et al., 2015 ), including contextual information ( Wu et al., 2013c; 2014b; Benisty et al., 2014 ), and local linear embeddings ( Wu et al., 2016 ).”
Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks (2017, result): “unreported because we found that it remained about the same as that of [13] (System B in [14]).”