Currently, the old feature extraction method, which was used early for speech recognition, is used in speaker recognition in our speaker recognition group. Standard Mell Frequency Cepstral Coefficients (MFCC) features are used. They can be extended by delta and acceleration coefficients eventually. Whereas features for speech recognition has been evolved and optimized until now, features for sp...

The multi—pulse excited linear prediction (MPELP) system is proposed for speech enhancement. It is shown that for successful enhancement of speech the error—weighting filter should not be used in the MPELP system. A new method (the constrained forward—backward correlation prediction method) is proposed for accurate estimation of LP coefficients from noisy speech. This method guarantees the stab...

In this paper, a new method for estimating the linear regression coefficients approximation is presented based on Z-numbers. In this model, observations are real numbers, regression coefficients and dependent variables (y) have values ​​for Z-numbers. To estimate the coefficients of this model, we first convert the linear regression model based on Z-numbers into two fuzzy linear regression mode...

Linear prediction plays a fundamental role in all aspects of speech. Its use seems natural and obvious since for a speech signal the value of its current sample can be well modeled as a linear combination of its past values. Calculation for predictor coefficients with the help of automatic code generation gives the solution for early and efficient computing. Automatic code generation is a fast ...

This thesis deals with developing improved techniques for speech coding based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the wellknown linear prediction (LP) model currently applied in many modern speech coders. In the first part of the thesis, we provide an overview of Sparse Linear Predict...

In this work, we study the problem of aggregating a finite number of predictors for non stationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: 1) a uniform bound of the `1 norm of the time-varying sub-linear coefficients, 2) a Lipschitz assumption on the predictors and 3) moment conditions on the noise appearing in the linear representation. Tw...

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant “à trous” wavelet transform which has the advantage of being shift-invariant. The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its...