8. A speech recognition method, the method comprising: calculating, using at least one processing device, a first confidence score using a ratio between a likelihood of a keyword model for feature vectors per frame of a speech signal and a likelihood of a Filler model for the feature vectors; calculating a second confidence score based on a determined similarity between a Gaussian distribution trace of the keyword model per frame of the speech signal and a Gaussian distribution trace sample of a stored corresponding keyword of the keyword model; determining a confidence of a result calculated using the keyword model in accordance with a determination of whether a position determined by the first and second confidence scores is included in a keyword area or in a non-keyword area on a confidence coordinate system including an axis of the first confidence score calculated using the likelihood ratio of the first likelihood and the second likelihood and another axis of the second confidence score calculated based on the determined similarity, wherein the calculating the second confidence score comprises assigning a weight value which given in accordance with sub-word, a serial number, and an order of the mixed waveform probability distributions, and in accordance with an arrangement order of the Gaussian distribution trace and the Gaussian distribution trace sample; and identifying whether the first and second confidence scores are to be used to generate a reference function or to be used to recognize the speech signal, and wherein at least one of a position and form of the generated reference function is updated based on the speech signal.
13. The method of claim 8 , wherein the keyword model is extracted using a Viterbi decoder.