@inproceedings{Pohjalainen2014bb,
title = {Gaussian mixture linear prediction},
author = {Jouni Pohjalainen and Paavo Alku},
year = {2014},
date = {2014-05-04},
booktitle = {Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on},
pages = {6285 - 6289},
publisher = {IEEE},
abstract = {This work introduces an approach to linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different autoregressive states of the model to approximately correspond to the target signal and the expected type of undesired signal components, such as background noise, the iterative parameter estimation converges towards a focused linear prediction model of the target signal. Differently initialized and trained variants of mixture linear prediction are evaluated using objective spectrum distortion measures as well as in feature extraction for speech detection in the presence of ambient noise. In these evaluations, the novel analysis methods perform better than the Fourier transform and conventional linear prediction},
keywords = {weighted linear prediction}
}

@article{Airaksinen2013b,
title = {Quasi closed phase glottal inverse filtering analysis with weighted linear prediction},
author = {Manu Airaksinen and Tuomo Raitio and Brad Story and Paavo Alku},
url = {http://dx.doi.org/10.1109/TASLP.2013.2294585},
year = {2014},
date = {2014-03-01},
issuetitle = {Quasi closed phase glottal inverse filtering analysis with weighted linear prediction},
journal = {IEEE Transactions on Audio, Speech, and Language Processing},
volume = {22},
number = {3},
pages = {596-607},
abstract = {This study presents a new glottal inverse filtering (GIF) technique based on closed phase analysis over multiple fundamental periods. The proposed quasi closed phase (QCP) analysis method utilizes weighted linear prediction (WLP) with a specific attenuated main excitation (AME) weight function that attenuates the contribution of the glottal source in the linear prediction model optimization. This enables the use of the autocorrelation criterion in linear prediction in contrast to the covariance criterion used in conventional closed phase analysis. The proposed method was compared to previously developed methods by using synthetic vowels produced with the conventional source-filter model as well as with a physical modeling approach. The obtained objective measures show that the QCP method improves the GIF performance in terms of errors in typical glottal source parametrizations for both lowand high-pitched vowels. Additionally, QCP was tested in a physiologically oriented vocoder, where the analysis/synthesis quality was evaluated with a subjective listening test indicating improved perceived quality for normal speaking style.},
keywords = {glottal inverse filtering, weighted linear prediction}
}

@inproceedings{Alku_et_al_interspeech2012,
title = {Improved formant frequency estimation from high-pitched vowels by downgrading the contribution of the glottal source with weighted linear prediction},
author = {Paavo Alku, Jouni Pohjalainen, Martti Vainio, Anne-Maria Laukkanen, Brad Story},
url = {http://consortium.simple4all.org/files/2012/10/Alku-et-al-interspeech2012.pdf},
issn = {1990-9770},
year = {2012},
date = {2012-10-12},
booktitle = {Proc. Interspeech 2012},
abstract = {Since performance of conventional linear prediction (LP) deteriorates in formant estimation of high-pitched voices, several all-pole modeling methods robust to F0 have been developed. This study compares five such previously known methods and proposes a new technique, Weighted Linear Prediction with Attenuated Main Excitation (WLP-AME). WLP-AME utilizes weighted linear prediction in which the square of the prediction error is multiplied with a weighting function that downgrades the contribution of the glottal source in the model optimization. Consequently, the resulting all-pole model is affected more by the vocal tract characteristics, which leads to more accurate formant estimates. By using synthetic vowels created with a physical modeling approach, the study shows that WLP-AME yields improved formant frequency estimates for high-pitched vowels in comparison to the previously known methods.},
keywords = {formants, linear prediction, weighted linear prediction}
}

@inproceedings{Pohjalainen2012b,
title = {Robust speech analysis by lag-weighted linear prediction},
author = {Jouni Pohjalainen and Paavo Alku},
isbn = {1520-6149},
year = {2012},
date = {2012-03-26},
booktitle = {Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on},
pages = {4453 - 4456},
publisher = {IEEE},
abstract = {This study introduces an approach for linear predictive spectrum analysis based on emphasizing selected time-domain properties in the analyzed signal in combination with a stabilization operation. A stable weighted linear predictive method based on a novel autocorrelation-based weighting scheme is described and its spectral properties are demonstrated. The robustness of the proposed method is compared with conventional techniques in terms of an Euclidean MFCC distortion measure in different additive noise conditions. In the experimental evaluation, the novel speech analysis technique outperforms the other evaluated methods.},
keywords = {weighted linear prediction}
}