Linear regression is probably the most popular model for predicting a RV Y ∈ R based on multiple RVs X1, . . . , Xd ∈ R. It predicts a numeric variable using a linear combination of variables ∑ θiXi where the combination coefficients θi are determined by minimizing the sum of squared prediction error on the training set. We use below the convention that the first variable is always one i.e., X1...

Temporally weighted linear predictive methods have recently been successfully used for robust feature extraction in speech and speaker recognition. This paper introduces their general formulation, where various efficient temporal weighting functions can be included in the optimization of the all-pole coefficients of a linear predictive model. Temporal weighting is imposed by multiplying element...

this paper presents a prediction model based on a new neuro-fuzzy algorithm for estimating time in construction projects. the output of the proposed prediction model, which is employed based on a locally linear neuro-fuzzy (llnf) model, is useful for assessing a project status at different time horizons. being trained by a locally linear model tree (lolimot) learning algorithm, the model is int...

Log area ratio coefficients (LAR) derived from linear prediction coefficients (LPC) is a well known feature extraction technique used in speech applications. This paper presents a novel way to use the LAR feature in a speaker identification system. Here, instead of using the mel frequency cepstral coefficients (MFCC), the LAR feature is used in a Gaussian mixture model (GMM) based speaker ident...

This paper presents our study on the feasibility and effectiveness of using the MELP (Mixed Excitation Linear Prediction) model for coding wideband (7KHz) speech signals at a transmission bit rate of 8kbps. In order to achieve a reasonably good subjective quality for the decoded speech while maintaining a low operating bit rate at the same time, modifications to the pitch estimation, LP analysi...

Recently, the authors introduced a novel algorithm for long range prediction of fading channels [1-4]. This algorithm finds the linear Minimum Mean Squared Error (MMSE) estimate of the future fading coefficients given a fixed number of previous observations. In this paper, we show that the superior performance of this algorithm is due to its lower sampling rate relative to the conventional (dat...

Analysis of speech signals can be performed with the aid of linear or nonlinear statistics using appropriate prediction algorithms. In this contribution, speech features are treated using the results of a nonlinear prediction based on Volterra series. Features are investigated representing the prediction gain by nonlinear statistics and representing individual coefficients of the nonlinear comp...

A novel approach to image compression using vector quantization of linear (one-step) prediction errors is presented in this paper. In order to minin,ize the image reconstruction error, we choose the optimum predictor coefficients (in a least-squares sense) that satisfy the additional constraint that the energy of the impulse response function of the inverse reconstruction filter is bounded by a...

In this paper, we test the applicability of state-of-the-art automatic speech recognition (ASR) to predict phoneme confusions in human listeners. Phoneme-specific response rates are obtained from ASR based on deep neural networks (DNNs) and from listening tests with six normal-hearing subjects. The measure for model quality is the correlation of phoneme recognition accuracies obtained in ASR an...

This paper presents an animated 2D articulation model of the tongue and the lips for biofeedback applications. The model is controlled by real-time optopalatographic measurements of the positions of the upper lip and the tongue in the anterior oral cavity. The measurement system is an improvement on a previous prototype with increased spatial resolution and an enhanced close-range behavior. The...