http://hdl.handle.net/2117/1604 2013-06-20T10:24:12Z http://hdl.handle.net/2117/2104 Title: N-gram-based Machine Translation Authors: Mariño Acebal, José Bernardo; Banchs Martínez, Rafael Enrique; Crego Clemente, Josep Maria; Gispert Brosa, Adrian de; Lambert, Patrik; Rodríguez Fonollosa, José Adrián; Ruiz Costa-Jussà, Marta Abstract: This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS). 2008-06-20T07:10:28Z http://hdl.handle.net/2117/2065 Title: Classification of acoustic events using SVM-based clustering schemes Authors: Temko, Andrey A.; Nadeu Camprubí, Climent Abstract: Acoustic events produced in controlled environments may carry information useful for perceptually aware interfaces. In this paper we focus on the problem of classifying 16 types of meeting-room acoustic events. First of all, we have defined the events and gathered a sound database. Then, several classifiers based on support vector machines (SVM) are developed using confusion matrix based clustering schemes to deal with the multi-class problem. Also, several sets of acoustic features are defined and used in the classification tests. In the experiments, the developed SVM-based classifiers are compared with an already reported binary tree scheme and with their correlative. Gaussian mixture model (GMM) classifiers. The best results are obtained with a tree SVM-based classifier that may use a different feature set at each node. With it, a 31.5% relative average error reduction is obtained with respect to the best result from a conventional binary tree scheme. 2008-06-04T10:59:51Z http://hdl.handle.net/2117/2064 Title: Fuzzy integral based information fusion for classification of highly confusable non-speech sounds Authors: Temko, Andrey A.; Macho Ciena, Dusan; Nadeu Camprubí, Climent Abstract: Acoustic event classification may help to describe acoustic scenes and contribute to improve the robustness of speech technologies. In this work, fusion of different information sources with the fuzzy integral (FI), and the associated fuzzy measure (FM), are applied to the problem of classifying a small set of highly confusable human non-speech sounds. As FI is a meaningful formalism for combining classifier outputs that can capture interactions among the various sources of information, it shows in our experiments a significantly better performance than that of any single classifier entering the FI fusion module. Actually, that FI decision-level fusion approach shows comparable results to the high-performing SVM feature-level fusion and thus it seems to be a good choice when feature-level fusion is not an option. We have also observed that the importance and the degree of interaction among the various feature types given by the FM can be used for feature selection, and gives a valuable insight into the problem. 2008-06-04T10:48:05Z