NASA Scientific and Technical Aerospace Reports

a product of two Toeplitz data matrices, as in the correlation method of linear prediction, then the Levinson recursions may
be used to derive the Cybenko recursions, and the Cybenko recursions may be used to derive the Le Roux-Gueguen recursions.
We explore the close relation between QR and Cholesky algorithms in the Toeplitz case and we compare their respective
numerical properties when run in finite precision arithmetic.
Author
Algorithms; Cholesky Factorization; Operators; Matrix Theory
20060001605 Oregon State Univ., Corvallis, OR, USA
On Identification of Non-Gaussian Time Series
Mohler, R. R.; Tang, Z.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume
1; 1987, pp. 2.8.1-2.8.4; In English; See also 20060001583
Contract(s)/Grant(s): N00014-81-K-6814; Copyright; Avail.: Other Sources
Affine bilinear time series models AB(p,q) are considered here. An ‘inverse method’ is used to estimate model parameters.
A non-anticipative AB(p,q) model could be transfered to 2p-1 dimension vector form (AB1,1) model. Simulation results for
computer-generated and real data are given.
Author
Time Series Analysis; Numerical Analysis
20060001606 University of Southern California, Los Angeles, CA, USA
ARMA Modeling Using Cumulant and Autocorrelation Statistics
Giannakis, G. B.; Mendel, J. M.; Wang, W.; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 2.9.1 - 2.9.4; In English; See also 20060001583
Contract(s)/Grant(s): NSF ECS-85-01098; NSF ECS-86-02531; Copyright; Avail.: Other Sources
One dimensional cumulant and autocorrelation output statistics are combined to form an overdetermined system of
equations whose least-squares solution yields the coefficients of an ARMA model. The driving input noise is assumed to be
non-Gaussian and white. The ARMA model is allowed to be non-minimum phase and even to contain all-pass factors. The
special cases of AR and MA models are also included. The overdetermined nature of the method makes the solution practical
for moderate output data lengths, when additive white Gaussian noise is considered. Simulations illustrate that our approach
performs very well even at low signal-to-noise ratios.
Author
Autoregressive Moving Average; Autocorrelation; Simulation
20060001607 BBN Systems and Technologies Corp., Cambridge, MA, USA
A Stochastic Segment Model for Phoneme-Based Continuous Speech
Roucos, S.; Dunham, M. O.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 3.3.1-3.3.4; In English; See also 20060001583
Contract(s)/Grant(s): N00014-85-C-0279; Copyright; Avail.: Other Sources
Developing accurate and robust phonetic models for the different speech sounds is a major challenge for high performance
continuous speech recognition. In this paper, we introduce a new approach, called the stochastic segment model, for modelling
a variable-length phonetic segment X, an L-long sequence of feature vectors. The stochastic segment model consists of
time-warping the variable-length segment X into a fixed-length segment Y called a resampled segment, and a joint density
function of the parameters of the resampled segment Y, which in this work is assumed Gaussian. In this paper, we describe
the stochastic segment model, the recognition algorithm, and the iterative training algorithm for estimating segment models
from continuous speech. For speaker-dependent continuous speech recognition, the segment model reduces the word error rate
by one third over a hidden Markov phonetic model.
Author
Phonemes; Speech Recognition; Stochastic Processes; Words (Language)
20060001618 California Univ., Davis, CA, USA
Object Classification and Registration by Radon Transform Based Invariants
Dohse, Hans J.; Sanz, Jorge L. C.; Jain, Anil K.; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 7.5.1-7.5.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
algorithm for object recognition which is based on translation and rotation invariant signatures is presented. The Radon
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