Abstract
Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping. An outer warping process, which temporally aligns blocks of speech (dynamic time warp), invokes an inner warping process, which spectrally aligns based on magnitude spectra (dynamic frequency warp). The mapping function produced by the dynamic frequency warp is used to move spectral information from a source speaker to a target speaker. Information obtained by this process is used to train an artificial neural network to produce spectral warping output information based on spectral input data.
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