inear prediction modelling is used in a diverse area of applications, such as data forecasting, speech coding, video coding, speech recognition, model-based spectral analysis, model-based interpolation, signal restoration, and impulse/step event detection. In the statistical literature, linear prediction models are often referred to as autoregressive (AR) processes. In this chapter, we introduc...

Linear prediction (LP) has become a standard tool for enhancing the appearance of multidimensional NMR spectra (1-7). In principle, the method can be used to calculate the frequencies, amplitudes, damping factors (linewidths), and phases of all components contained in the time-domain signal. In practice, however, the method is not very robust in the presence of noise. For this reason, a more co...

This paper deals with automatic speech recognition in Czech. We focus here on context independent speaker recognition with a closed set of speakers. To the best of our knowledge, there is no comparative study about different speaker recognition approaches on the Czech language. The main goal of this paper is thus to evaluate and compare several parametrization/classification methods in order to...

Digital sync signal processing for asynchronously digitized video signals is presented. A simplified matched filter for sync extraction and a linear prediction method for sync pulse filtering improves image stability significantly in comparison with a conventional phase-locked-loop (PLL) approach. Due to the linear prediction method, additional adaption to phase skips in the input signal (non-s...

In the aim of developing the assessment of speech disorders for detecting patients with Parkinson’s disease (PD), we have collected 34 sustained vowel / a /, from 34 subjects including 17 PD patients. We subsequently extracted from 1 to 20 coefficients of the Perceptual Linear Prediction (PLP) from each individual. To extract the voiceprint from each individual, we compressed the frames by calc...

In this paper we present a complex linear prediction analysis method for estimating the formant frequencies of noisy speech. The proposed method effectively utilizes the signal (being the analytic signal) which is ignored in the conventional complex linear prediction analysis to achieve noise reduction. Also, the covariance and forward-backward linear prediction (FBLP) methods are compared, and...

Automatic Speech Recognition Systems of today are intensely deployed in real world application scenarios which are often characterized by suboptimal operating conditions. Thus their noise robustness has become a crucial parameter when assessing ASR in-field performance. The paper examines the noise robustness of traditional ASR feature sets as applied to a Voice Dialing Application built for Ma...

Speaker Recognition (SR) is an economic method of biometrics because of availability of low cost and high power computers. An important question which must be answered for the SR system is how well the system resists the effects of determined mimics such as those based on physiological characteristics especially identical twins or triplets. In this paper, a new data fusion technique (viz., majo...

This paper describes the generalized lattice model of human vocal tract for speech analysis, relating it to the all pole type linear prediction. A more natural synthesized speech can be achieved using Auto Regressive (AR) lattice model. Speech analysis is done using linear prediction. The structure of AR lattice model is numerically stable, which is main requirement in speech analysis and synth...

This thesis is concerned with the extension of the theory and computational techniques of time-series linear prediction to two-dimensional (2-D) random processes. 2-D random processes are encountered in image processing, array processing, and generally wherever data is spatially dependent. The fundamental problem of linear prediction is to determine a causal and causally invertible (minimumphas...