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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 func-tions can be included in the optimization of the all-pole co-efficients of a linear predictive model. Temporal weighting is imposed by multiplying elements of instantaneous autocorrela-tion "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.
Original languageEnglish
Title of host publicationINTERSPEECH 2013, 14th Annual Conference of the International Speech Communication Association
Subtitle of host publicationLyon, France, August 25-29, 2013
PublisherInternational Speech Communication Association
Pages1931-1935
Number of pages5
Publication statusPublished - 2013

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