Random discriminative projection based feature selection with application to conflict recognition
IEEE Signal Processing Letters, cilt.22, sa.6, ss.671-675, 2015 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 22 Sayı: 6
- Basım Tarihi: 2015
- Doi Numarası: 10.1109/lsp.2014.2365393
- Dergi Adı: IEEE Signal Processing Letters
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.671-675
- Anahtar Kelimeler: CCA, computational paralinguistics, discriminative projection, feature selection, random projection
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Computational paralinguistics deals with underlying meaning of the verbal messages, which is of interest in manifold applications ranging from intelligent tutoring systems to affect sensitive robots. The state-of-the-art pipeline of paralinguistic speech analysis utilizes brute-force feature extraction, and the features need to be tailored according to the relevant task. In this work, we extend a recent discriminative projection based feature selection method using the power of stochasticity to overcome local minima and to reduce the computational complexity. The proposed approach assigns weights both to groups and to features individually in many randomly selected contexts and then combines them for a final ranking. The efficacy of the proposed method is shown in a recent paralinguistic challenge corpus to detect level of conflict in dyadic and group conversations. We advance the state-of-the-art in this corpus using the INTERSPEECH 2013 Challenge protocol.