A recognition method with parametric trajectory synthesized using direct relations between static and dynamic feature vector time series
Yasuhiro Minami, Erik McDermott, Atsushi Nakamura, Shigeru Katagiri · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
Parametric trajectory models have been proposed to exploit this time-dependency. However, parametric trajectory modeling methods are unable to take advantage of efficient HMM training and recognition methods. We have proposed a new speech recognition technique that generates a speech trajectory using an HMM-based speech synthesis method. This method generates an acoustic trajectory by maximizing the likelihood of the trajectory while taking into account the relation between the cepstrum, delta-cepstrum, and delta-delta cepstrum. In this paper, we extend our method to a general formulation including variance training procedure. Speaker independent speech recognition experiments show that the proposed method is effective for speech recognition.