Automatic emotion recognition from speech is limited by the ability to discover the relevant predicting features. The common approach is to extract a very large set of features over a generally long analysis time window. In this paper we investigate the applicability of two-sample Kolmogorov-Smirnov statistical test (KST) to the problem of segmental speech emotion recognition. We train emotion classifiers for each speech segment within an utterance. The segment labels are then combined to predict the dominant emotion label. Our findings show that KST can be successfully used to extract statistically relevant features. KST criterion is used to optimize the parameters of the statistical segmental analysis, namely the window segment size and shift. We carry out seven binary class emotion classification experiments on the Emo-DB and evaluate the impact of the segmental analysis and emotion-specific feature selection.
Scheda prodotto non validato
I dati visualizzati non sono stati ancora sottoposti a validazione formale da parte dello Staff di IRIS, ma sono stati ugualmente trasmessi al Sito Docente Cineca (Loginmiur).
Titolo: | Kolmogorov-Smirnov Test for Feature Selection in Emotion recognition from Speech | |
Autori: | Ivanou, Aliaksei; Riccardi, Giuseppe | |
Autori Unitn: | ||
Luogo di edizione: | Washington | |
Casa editrice: | IEEE | |
Anno di pubblicazione: | 2012 | |
Titolo del volume contenente il saggio: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | |
Codice identificativo Scopus: | 2-s2.0-84867593857 | |
Codice identificativo WOS: | WOS:000312381405050 | |
ISBN: | 9781467300469 | |
Handle: | http://hdl.handle.net/11572/92177 | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/ICASSP.2012.6289074 | |
Appare nelle tipologie: | 04.3 Poster presentato a convegno (Poster presented at Conference or Workshop) |