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Autoregressive HMM modeling of spectral features has been proposed as a replacement for standard HMM speech synthesis. The merits of the approach are explored, and methods for enforcing stability of the estimated predictor coefficients are presented. It appears that rather than directly estimating autoregressive HMM parameters, greater synthesis accuracy is obtained by estimating the autoregressive...
Preliminary results are reported from a very simple speech-synthesis system based on clustered-diphone Kalman Filter based modeling of line-spectral frequency based features. Parameters were estimated using maximum-likelihood EM training, with a constraint enforced that prevented eigenvalue magnitudes in the transition matrix from exceeding 1. Frames of training data were assigned diphone unit labels...
This paper describes the BYBLOS system that BBN used to participate in the 2001 NIST Hub-5 evaluation benchmark. We outline the procedure used for training and decoding, and present the algorithmic improvements made to the system, along with experimental results. These improvements include a Gaussian splitting initialization procedure, the use of Linear Discriminant Analysis, and processing of additional...
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