The paper is focused on the friendliness analysis and perception of dialogue speech. To do that, the paper uses a concept of the "perception vector" which contains the information of emotions and softness. In creating the "perception vector", and to simulate the perception ambiguity, the paper allows the listeners to label the speech with multiple emotions, and align them into "one choice", "first choice" and "second choice". Then, the paper makes the correlation analysis between friendliness and "perception vectors", the results disclose that the friendliness is positive correlation to "softness", "happiness" and "anger". Finally the paper traines a classification tree model to predict friendliness degree from acoustic features. With the classification tree model, we get the ranking scores of the acoustic parameters’ importance for perceptually synthesized speech. Results shows that the F0 mean assumes the most important role in emotion perception, Ee is the most important parameter related to voice quality for the perception model.