| 摘要 |
PURPOSE:To attain a high recognition rate by less learning by using a universal code book for unspecified speakers at the time of unspecified speaker rough vocabulary speech recognition. CONSTITUTION:A word/phoneme hidden Markov model(HMM) generated according to speech spectra from many speakers has transition in the order of three states A, B, and C from the left to the right; and the transition probability from the state A to the state B is denoted as tAB and the probability of output of a word/phoneme feature vector yt from the state A to the state B at a point (t) of time is denoted as PAB(y1). Similarly, relatively short learnt speeches are inputted, speaker by speaker, and a word/phoneme ergodic hidden Markov model (speaker HMM) generated according to the speech spectra has transition between two states 1 and 2; and the probability of transition from the state 1 to the state 2 is t12 and the probability of the output of the feature vector yt from the state 1 to the state 2 at a point (t) of time is P12(y1). At this time, the HMM (figure 1) in the product space of those unspecified speaker HMM and speaker HMM is generated and the HMM in the product space is used to recognize the input voice of the speaker. |
