1. A method of evaluating naturalness of speech utterances by using crowd wisdom models, the method comprising: presenting a plurality of human-testers with obtained speech utterances; receiving, for each presented speech utterance, a plurality of corresponding human-tester generated speech utterances being human repetitions of the presented speech utterance; generating, for each presented speech utterance, an utterance-specific scoring model that is based on the corresponding human-tester generated speech utterances, each scoring model being configured to estimate a level of speech naturalness for the presented speech utterance using at least crowd wisdom models; and deriving a speech naturalness score for each presented speech utterance by respectively applying the utterance-specific scoring model to each presented speech utterance, wherein at least one of: the presenting, the receiving, the generating, and the deriving is executed by at least one computer processor.
1. A method of evaluating naturalness of speech utterances by using crowd wisdom models, the method comprising: presenting a plurality of human-testers with obtained speech utterances; receiving, for each presented speech utterance, a plurality of corresponding human-tester generated speech utterances being human repetitions of the presented speech utterance; generating, for each presented speech utterance, an utterance-specific scoring model that is based on the corresponding human-tester generated speech utterances, each scoring model being configured to estimate a level of speech naturalness for the presented speech utterance using at least crowd wisdom models; and deriving a speech naturalness score for each presented speech utterance by respectively applying the utterance-specific scoring model to each presented speech utterance, wherein at least one of: the presenting, the receiving, the generating, and the deriving is executed by at least one computer processor. 4. The method according to claim 1 , wherein the generating comprises filtering out at least some of the human testers-generated utterances which exhibit speech naturalness score below a specified threshold such that the filtered out testers-generated utterances are not used in the updating of the scoring model.