12. A system comprising: one or more processors; memory; and machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out functions including: implementing a source hidden Markov model (HMM) based speech features generator by one or more processors of a system, wherein the source HMM based speech features generator comprises a configuration of source HMM state models, each of the source HMM state models having a set of generator-model functions, and wherein the implemented source HMM based speech features generator is trained using speech signals of a source speaker; providing a set of target-speaker vectors, the set of target-speaker vectors having been generated from speech features extracted from speech signals of a target speaker; implementing a converted HMM based speech features generator that is the same as the source HMM based speech features generator, but wherein (i) parameters of the set of generator-model functions of each given source HMM state model of the converted HMM based speech features generator are replaced with a particular target-speaker vector from among the target set that most closely matches the parameters of the set of generator-model functions of the given source HMM, and (ii) fundamental frequency (F0) statistics of the converted HMM based speech features generator are speech-adapted using an F0 transform that speech-adapts F0 statistics of the source HMM based speech features generator to match F0 statistics of the speech of the target speaker.
12. A system comprising: one or more processors; memory; and machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out functions including: implementing a source hidden Markov model (HMM) based speech features generator by one or more processors of a system, wherein the source HMM based speech features generator comprises a configuration of source HMM state models, each of the source HMM state models having a set of generator-model functions, and wherein the implemented source HMM based speech features generator is trained using speech signals of a source speaker; providing a set of target-speaker vectors, the set of target-speaker vectors having been generated from speech features extracted from speech signals of a target speaker; implementing a converted HMM based speech features generator that is the same as the source HMM based speech features generator, but wherein (i) parameters of the set of generator-model functions of each given source HMM state model of the converted HMM based speech features generator are replaced with a particular target-speaker vector from among the target set that most closely matches the parameters of the set of generator-model functions of the given source HMM, and (ii) fundamental frequency (F0) statistics of the converted HMM based speech features generator are speech-adapted using an F0 transform that speech-adapts F0 statistics of the source HMM based speech features generator to match F0 statistics of the speech of the target speaker. 15. The system of claim 12 , wherein the set of generator-model functions for each given source HMM state model comprises a multivariate spectral probability density function (PDF) for jointly modeling spectral envelope parameters of a phonetic unit modeled by a given source HMM state model, and a multivariate excitation PDF for jointly modeling excitation parameters of the phonetic unit, and wherein determining for each given source HMM state model the particular target-speaker vector from among the target set that most closely matches parameters of the set of generator-model functions of the given source HMM comprises: determining a target-speaker vector from among the target set that is computationally nearest to parameters of the multivariate spectral PDF of the given source HMM state model in terms of a distance criterion based on one of mean-squared-error (mse) or Kullback-Leibler distance; and determining a target-speaker vector from among the target set that is computationally nearest to the multivariate excitation PDF of the given source HMM state model in terms of a distance criterion based on one of mse or Kullback-Leibler distance.