10. A weighting factor learning method comprising: an audio recognition operation of recognizing learning audio data and outputting a recognition result; a weighting factor updating operation of updating a first weighting factor applied to an acoustic score obtained from an acoustic model and a second weighting factor applied to a language score obtained from a language model which are used in the audio recognition so that a difference between a correct-answer score calculated with the use of a correct-answer text of the learning audio data and a recognition-result score output by the audio recognition operation becomes large; a convergence determination operation of determining, with the use of the acoustic score after updating the first weighting factor and the language score after updating the second weighting factor, whether to return to the weighting factor updating operation to update the first weighting factor and the second weighting factor again; and a weighting factor convergence determination operation of determining, with the use of the acoustic score after updating the first weighting factor and the language score after updating the second weighting factor, whether to return to the audio recognition operation to recognize the learning audio data and to output the recognition result using the audio recognition operation, update the first weighting factor and the second weighting factor in the weighting factor updating operation, and determine whether to return to the weighting factor updating operation using the convergence determination operation to update the first weighting factor and the second weighting factor again.
14. The weighting factor learning method according to claim 10 , wherein the recognition result is a word sequence whose differences from the largest score in the acoustic score, the language score and the sum of the acoustic and language scores are less than predetermined thresholds.