Memory for object locations: Priority effect and sex differences in associative spatial learning

This paper reports two experiments conducted to examine priority effects and sex differences in object location memory. A new task of paired position-learning was designed, based on the A-B A-C paradigm, which was used in paired word learning. There were three different paired position-learning conditions: (1) positions of several different objects (B-objects and C-objects) around referent objects (A-objects) were learned in the A-B A-C position-learning condition, (2) positions of several different objects with no referent objects were learned in the 0-B 0-C position-learning condition, and (3) positions of identical objects (stars) with no referent objects were learned in the 0-star 0-star position-only condition. The results revealed a significant priority effect on performance in the A-B A-C and the 0-B 0-C position-learning conditions but not in the 0-star 0-star position-only condition. Contradictory results were obtained with respect to the sex variable: a female superiority effect on paired position learning was significant in Experiment 1, but this effect was not replicated in Experiment 2. In addition, an articulatory suppression task used in Experiment 2 had a significant effect on recall of different object positions but no effect on recall of identical object positions. This suggested that verbal encoding was not necessary for learning of positions of identical objects. (C) 2007 Elsevier Inc. All rights reserved

C-Means Clustering Applied to Speech Discrimination

An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The proposed speech/pause discrimination method is based on a hard-decision clustering approach built over a set of subband log-energies. Detecting the presence of speech frames (a new cluster) is achieved using a basic sequential algorithm scheme (BSAS) according to a given “distance” (in this case, geometrical distance) and a suitable threshold. The accuracy of the Cl-VAD algorithm lies in the use of a decision function defined over a multiple-observation (MO) window of averaged subband log-energies and the modeling of noise subspace into cluster prototypes. In addition, time efficiency is also reached due to the clustering approach which is fundamental in VAD real time applications, i.e. speech recognition. An exhaustive analysis on the Spanish SpeechDat-Car databases is conducted in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs and a representative set of recently reported VAD algorithms