NASA Scientific and Technical Aerospace Reports

curve and a distance parameter ‘p’ such that the product of distances of any point on the shape from the foci is approximately
constant and is equal to ‘p’. The computation of the foci co-ordinates and ‘p’ parameter has been presented as a solution to
a linearized least-square problem. The reconstruction algorithm is based on straightforward implementation of the theorem.
Some experimental results have also been provided indicating the success of the algorithm.
Author
Algorithms; Coding; Theorems; Foci; Shapes; Algebra
20060001718 American Telephone and Telegraph Co., NJ, USA
Robust Linear Prediction for Speech Analysis
Lee, Chin-Hui; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 8.1.1-8.1.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
In this paper, a robust linear prediction algorithm is proposed. Rather than minimizing the sum of squared residuals as
in the conventional linear prediction procedures, the robust LP procedure minimizes the sum of appropriately weighted
residuals. The weight is a function of the prediction residual, and the cost function is selected to give more weight to the bulk
of smaller residuals while de-weighting the small portion of large residuals. Based on Robustness Theory. the proposed
algorithm will always give a more efficient (lower variance) estimate for the prediction coefficients if the excitation source is
of Gaussian mixture such that a large portion of the excitations are from a normal distribution with a very small variance while
a small portion of the excitations at the glottal openings and closures are from some unknown distribution with a much larger
variance. The robust LP algorithm can be used in the front-end feature extractor for a speech recognition system and as an
analyzer for a speech coding system. Testing on synthetic vowel data demonstrates that the robust LP procedure is able to
reduce the formant and bandwidth error rate by more than an order of magnitude compared to the conventional LP procedures.
Preliminary experiments on natural speech data indicate that the robust LP procedure is relatively insensitive to the placement
of the LPC analysis window and to the value of the pitch period, for a given section of speech signal.
Author
Speech Recognition; Prediction Analysis Techniques; Linear Prediction; Voice Data Processing; Robustness (Mathematics);
Normal Density Functions
20060001719 Indian Inst. of Tech., Madras, India
Reconstruction From Fourier Transform Phase with Applications to Speech Analysis
Yegnanarayana, B.; Tanveer Fathima, S.; Murthy, Hema A.; IEEE International Conference on Acoustics, Speech, and Signal
Processing (ICASSP ‘87); Volume 1; 1987, pp. 8.4.1-8.4.4; In English; See also 20060001583; Copyright; Avail.: Other
Sources
This paper addresses the problem of signal reconstruction from Fourier transform phase. In particular, we examine two
aspects of this problem. First, we discuss signal reconstruction from the phase spectrum of the short-time Fourier
transform(STFT). Next, we examine the problem of signal recovery from partial phase information. We present the results of
our studies on reconstruction from partial phase and discuss the application of these results in speech analysis and coding.
Author
Fourier Transformation; Signal Processing; Voice Data Processing
20060001736 Informasjonskontroll A/S, Asker, Norway
Objective Methods for Comparing Autoregressive Order-Determining Criteria
Holm, Sverre; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1; 1987,
pp. 9.6.1 - 9.6.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
Distance measures between a reference signal and the autoregressive estimate are used as an objective reference for
comparing autoregressive order-determining criteria. The distance measures discussed are the rms log spectral deviation, its
normalized cepstral distance approximation, the normalized autoregressive transfer function error, the equivalent Itakura
distance, and the average squared prediction error. Using them it is found that the AIC criterion is one of the best criteria and
performs better than the consistent Schwartz’ and Hannan and Quinn’s criteria.
Author
Predictions; Estimates; Cepstral Analysis; Transfer Functions; Root-Mean-Square Errors
20060001739 Memorial Univ. of Newfoundland, Saint Johns, Newfoundland, Canada
Extended Matrix Formulation for the Marple Algorithm
Vetter, William J.; Porsani, Milton J.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP
‘87); Volume 1; 1987, pp. 9.5.1-9.5.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
119