A method for automatic text-to-speech, TTS, performance evaluation of different TTS engines, comprising: providing a plurality of speech samples and scores associated with the respective speech samples; pre-processing the plurality of speech samples so as to obtain respective waveforms; extracting features from each of the pre-processed waveforms; establishing a speech model based on said features extracted from the pre-processed waveforms obtained from the plurality of speech samples and the corresponding scores; and evaluating a TTS engine by the speech model; wherein the step of providing further comprises: recording the plurality of speech samples from a plurality of speech sources based on a same set of training text and rating each of the plurality of speech samples to assign the score thereto; wherein the plurality of speech sources includes a plurality of TTS engines and human beings with different dialects and different clarity of pronunciation; and wherein the step of evaluating further comprises: providing a set of test sentences to the TTS engine under evaluation; receiving speeches converted by the TTS engine under evaluation from the set of test sentences; and computing an evaluation score for each piece of speeches based on the established speech model, wherein a simple test sentence has a lower weight and a complex test sentence has a higher weight, and wherein each evaluation score is multiplied by the respective lower and higher weights, before being summed up for determining a final evaluation score. providing a plurality of speech samples and scores associated with the respective speech samples; pre-processing the plurality of speech samples so as to obtain respective waveforms; extracting features from each of the pre-processed waveforms; establishing a speech model based on said features extracted from the pre-processed waveforms obtained from the plurality of speech samples and the corresponding scores; and evaluating a TTS engine by the speech model; wherein the step of providing further comprises: recording the plurality of speech samples from a plurality of speech sources based on a same set of training text and rating each of the plurality of speech samples to assign the score thereto; wherein the plurality of speech sources includes a plurality of TTS engines and human beings with different dialects and different clarity of pronunciation; and wherein the step of evaluating further comprises: providing a set of test sentences to the TTS engine under evaluation; receiving speeches converted by the TTS engine under evaluation from the set of test sentences; and computing an evaluation score for each piece of speeches based on the established speech model, wherein a simple test sentence has a lower weight and a complex test sentence has a higher weight, and wherein each evaluation score is multiplied by the respective lower and higher weights, before being summed up for determining a final evaluation score. recording the plurality of speech samples from a plurality of speech sources based on a same set of training text and rating each of the plurality of speech samples to assign the score thereto; wherein the plurality of speech sources includes a plurality of TTS engines and human beings with different dialects and different clarity of pronunciation; and wherein the step of evaluating further comprises: providing a set of test sentences to the TTS engine under evaluation; receiving speeches converted by the TTS engine under evaluation from the set of test sentences; and computing an evaluation score for each piece of speeches based on the established speech model, wherein a simple test sentence has a lower weight and a complex test sentence has a higher weight, and wherein each evaluation score is multiplied by the respective lower and higher weights, before being summed up for determining a final evaluation score. providing a set of test sentences to the TTS engine under evaluation; receiving speeches converted by the TTS engine under evaluation from the set of test sentences; and computing an evaluation score for each piece of speeches based on the established speech model, wherein a simple test sentence has a lower weight and a complex test sentence has a higher weight, and wherein each evaluation score is multiplied by the respective lower and higher weights, before being summed up for determining a final evaluation score.