In Voice Conversion (VC), the speech of a source speaker is modified to resemble that of a particular target speaker. Currently, standard VC approaches use Gaussian mixture model (GMM)-based transformations that do not generate high-quality converted speech due to “over-smoothing” resulting from weak links between individual source and target frame parameters. Dynamic Frequency Warping (DFW) offers an appealing alternative to GMM-based methods, as more spectral details are maintained in transformation; however, the speaker timbre is less successfully converted because spectral power is not adjusted explicitly. Previous work combines separate GMM- and DFW-transformed spectral envelopes for each frame. This paper proposes a more effective DFW-based approach that 1) does not rely on the baseline GMM methods, and 2) functions on the acoustic class level. To adjust spectral power, an amplitude scaling function is used that compares the average target and warped source log spectra for each acoustic class. The proposed DFW with Amplitude scaling (DFWA) outperforms standard GMM and hybrid GMM-DFW methods for VC in terms of both speech quality and timbre conversion, as is confirmed in extensive objective and subjective testing. Furthermore, by not requiring time-alignment of source and target speech, DFWA is able to perform equally well using parallel or nonparallel corpora, as is demonstrated explicitly.