Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Model Parameter Estimation for Mixture Density Polynomial Segment Models
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Design of tandem architecture using segmental trend features
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
A multi-resolution hidden Markov model using class-specific features
IEEE Transactions on Signal Processing
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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In this paper, we propose a new acoustic model for characterizing segmental features and an algorithm based upon a general framework of hidden Markov models (HMMs). The segmental features are represented as a trajectory of observed vector sequences by a polynomial regression function. To obtain the polynomial trajectory from speech segments, we modify the design matrix to include transitional information for contiguous frames. We also propose methods for estimating the likelihood of a given segment and trajectory parameters. The observation probability of a given segment is represented as the relation between the segment likelihood and the estimation error of the trajectories. The estimation error of a trajectory is considered the weight of the likelihood of a given segment in a state. This weight represents the probability of how well the corresponding trajectory characterizes the segment. The proposed model can be regarded as a generalization of a conventional HMM and a parametric trajectory model. We conducted several experiments to establish the effectiveness of the proposed method and the characteristics of the segmental features. The recognition results on the TIMIT database demonstrate that the performance of segmental-feature HMM (SFHMM) is better than that of a conventional HMM.