Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification Stefan Scherer a,c,∗, John Kane b, Christer Gobl b, Friedhelm Schwenker c a University of Southern California, Institute for Creative Technologies, 90094 Playa Vista, CA, United States b Trinity College Dublin, Phonetics and Speech Laboratory, School of Linguistic, Speech and Communication Sci...

In this paper we present our teamwork main results in the area of the automatic speech recognition and understanding in Romanian language using hidden Markov models (HMM), artificial neural networks (multilayer perceptron, support vector machines, Kohonen networks) and hybrid models (fuzzy HMM, fuzzy multilayer perceptron, HMM/multilayer perceptron) for different small Romanian language corpora...

In this paper, we present a comparative study on applications of Neuro Fuzzy and Support Vector Machines (SVMs) for pattern recognition. Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its numerous applic...

In this paper, we present our approach to SemEval-2013 Task 9.2. It is a feature rich classification using LIBSVM for Drug-Drug Interactions detection in the BioMedical domain. The features are extracted considering morphosyntactic, lexical and semantic concepts. Tools like openDMAP and TEES are used to extract semantic concepts from the corpus. The best F-score that we got for DrugDrug Interac...