1. A method of recognizing speech by measuring confidence levels of respective frames, the method comprising: obtaining frequency features of a received speech signal for the respective frames having a predetermined length; calculating a keyword model-based likelihood and a filler model-based likelihood for each of the frames; calculating confidence scores based on the two types of likelihoods for each of the frames; transforming the confidence scores by applying transform functions of clusters, which include the confidence scores or are close to the confidence scores, to the confidence scores; and deciding whether the received speech signal corresponds to a keyword or a non-keyword based on a combination of confidence scores of each frame, wherein the frames constitute a subword, and wherein the deciding comprises the operation of calculating skewness of a distribution of the confidence scores and correcting the confidence scores through an operation of subtracting a result, which is acquired by multiplying the calculated skewness by a constant, from the confidence scores.