3 accepted papers
We present a dictionary-learning algorithm for reducing the sparse coding residual of an exemplar-based method for native-to-nonnative voice conversion (VC). The proposed algorithm iteratively updates the source and target speaker dictionaries to reduce both the residual and voice conversion error,…
In previous work we presented a Sparse, Anchor-Based Representation of speech (SABR) that uses phonemic “anchors” to represent an utterance with a set of sparse non-negative weights. SABR is speaker-independent: combining weights from a source speaker with anchors from a target speaker can be used f…
We describe a method for adapting a physical vocal tract model's anatomical and gestural parameters using acoustic information to match a target speaker. Physical vocal tract models are hard to adjust to match a speaker, as doing so requires information which is difficult to capture, such as X-Ray o…