13. The system of claim 12 , wherein the functions further include: creating an enriched transcription of a run-time text string; using the speech-adapted converted HMM based speech features generator to convert the enriched transcription into corresponding output speech features; and generating a synthesized utterance of the enriched transcription using the output speech features, the synthesized utterance having voice characteristics 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.