ISCA Archive Interspeech 2013
ISCA Archive Interspeech 2013

Extended weighted linear prediction using the autocorrelation snapshot — a robust speech analysis method and its application to recognition of vocal emotions

Jouni Pohjalainen, Paavo Alku

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 elements of instantaneous autocorrelation "snapshot" matrices computed from speech data. With this novel autocorrelation-snapshot formulation of weighted linear prediction, it is demonstrated that different temporal aspects of speech can be emphasized in order to enhance robustness of feature extraction in speech emotion recognition.


doi: 10.21437/Interspeech.2013-23

Cite as: Pohjalainen, J., Alku, P. (2013) Extended weighted linear prediction using the autocorrelation snapshot — a robust speech analysis method and its application to recognition of vocal emotions. Proc. Interspeech 2013, 1931-1935, doi: 10.21437/Interspeech.2013-23

@inproceedings{pohjalainen13_interspeech,
  author={Jouni Pohjalainen and Paavo Alku},
  title={{Extended weighted linear prediction using the autocorrelation snapshot — a robust speech analysis method and its application to recognition of vocal emotions}},
  year=2013,
  booktitle={Proc. Interspeech 2013},
  pages={1931--1935},
  doi={10.21437/Interspeech.2013-23}
}