10. A server usable for evaluating naturalness of speech utterances by using crowd wisdom models, the server comprising: a features extraction module; a modeler; a scoring module; and a client-server communication module, wherein the server is in communication with a plurality of client devices that have user interface modules that are each controlled by the client-server communication module, wherein the client-server communication module is configured to: present, via the client devices, a plurality of human-testers with some of the obtained speech utterances, and receive, via the client devices, for each presented speech utterance, a plurality of corresponding human testers generated speech utterances being repetitions of the presented speech utterance, wherein the features extraction module is configured to extract speech features from speech utterances, wherein the modeler is configured to: generate, for each presented speech utterance, an utterance-specific scoring model for each one of a plurality of obtained speech utterances, that is based on respective the corresponding human-tester generated speech utterances, each scoring model being configured to estimate a level of speech naturalness for the presented speech utterances using at least crowd wisdom models, and update the utterance-specific scoring model based on the speech features extracted from the received speech utterances, and wherein the scoring module is configured to derive a speech naturalness score for each presented speech utterance, by applying the updated utterance-specific scoring model to each presented speech utterance, wherein at least one of: the features extraction module, the modeler, the scoring module and the client-server communication module is at least partially embodied in hardware.
10. A server usable for evaluating naturalness of speech utterances by using crowd wisdom models, the server comprising: a features extraction module; a modeler; a scoring module; and a client-server communication module, wherein the server is in communication with a plurality of client devices that have user interface modules that are each controlled by the client-server communication module, wherein the client-server communication module is configured to: present, via the client devices, a plurality of human-testers with some of the obtained speech utterances, and receive, via the client devices, for each presented speech utterance, a plurality of corresponding human testers generated speech utterances being repetitions of the presented speech utterance, wherein the features extraction module is configured to extract speech features from speech utterances, wherein the modeler is configured to: generate, for each presented speech utterance, an utterance-specific scoring model for each one of a plurality of obtained speech utterances, that is based on respective the corresponding human-tester generated speech utterances, each scoring model being configured to estimate a level of speech naturalness for the presented speech utterances using at least crowd wisdom models, and update the utterance-specific scoring model based on the speech features extracted from the received speech utterances, and wherein the scoring module is configured to derive a speech naturalness score for each presented speech utterance, by applying the updated utterance-specific scoring model to each presented speech utterance, wherein at least one of: the features extraction module, the modeler, the scoring module and the client-server communication module is at least partially embodied in hardware. 12. The server according to claim 10 , further comprising a testers-modeler; and a similarity analysis module, wherein the testers-modeler is configured to generate human tester-specific acoustic and prosodic models usable for measuring acoustic and prosodic distances between different human testers, and wherein the similarity analysis module is configured to use the acoustic and prosodic distances to generate information incentives for the human testers.