1. A method comprising: training an output hidden Markov model (HMM) based speech features generator implemented by one or more processors of a system using speech signals of an output speaker speaking an output language, wherein the output HMM based speech features generator comprises a first configuration of output HMM state models, each of the output HMM state models having a set of generator-model functions; training an auxiliary HMM based speech features generator implemented by one or more processors of the system using speech signals of an auxiliary speaker speaking an input language, wherein the auxiliary HMM based speech features generator comprises a second configuration of auxiliary HMM state models, each of the auxiliary HMM state models having a set of generator-model functions; for each given output HMM state model of the first configuration, determining a particular set of generator-model functions from among the auxiliary HMM state models of the second configuration that most closely matches the set of generator-model functions of the given output HMM; determining a fundamental frequency (F0) transform that speech-adapts F0 statistics of the output HMM based speech features generator to match F0 statistics of the auxiliary HMM based speech features generator; determining a duration transform that speech-adapts duration statistics of the output HMM based speech features generator to match duration statistics of the auxiliary HMM based speech features generator; constructing a chimaera HMM based speech features generator implemented by one or more processors of the system to be the same as the output HMM based speech features generator, but wherein the set of generator-model functions of each output HMM state model of the chimaera HMM based speech features generator is replaced with the determined particular most closely matching set of generator-model functions; and speech-adapting F0 statistics and duration statistics of the chimaera HMM based speech features generator using the F0 transform and duration transform, respectively.
1. A method comprising: training an output hidden Markov model (HMM) based speech features generator implemented by one or more processors of a system using speech signals of an output speaker speaking an output language, wherein the output HMM based speech features generator comprises a first configuration of output HMM state models, each of the output HMM state models having a set of generator-model functions; training an auxiliary HMM based speech features generator implemented by one or more processors of the system using speech signals of an auxiliary speaker speaking an input language, wherein the auxiliary HMM based speech features generator comprises a second configuration of auxiliary HMM state models, each of the auxiliary HMM state models having a set of generator-model functions; for each given output HMM state model of the first configuration, determining a particular set of generator-model functions from among the auxiliary HMM state models of the second configuration that most closely matches the set of generator-model functions of the given output HMM; determining a fundamental frequency (F0) transform that speech-adapts F0 statistics of the output HMM based speech features generator to match F0 statistics of the auxiliary HMM based speech features generator; determining a duration transform that speech-adapts duration statistics of the output HMM based speech features generator to match duration statistics of the auxiliary HMM based speech features generator; constructing a chimaera HMM based speech features generator implemented by one or more processors of the system to be the same as the output HMM based speech features generator, but wherein the set of generator-model functions of each output HMM state model of the chimaera HMM based speech features generator is replaced with the determined particular most closely matching set of generator-model functions; and speech-adapting F0 statistics and duration statistics of the chimaera HMM based speech features generator using the F0 transform and duration transform, respectively. 2. The method of claim 1 , wherein the output HMM based speech features generator, the auxiliary HMM based speech features generator, and the chimaera HMM based speech features generator are implemented by at least one common processor from among the one or more processors of the system.