A new approach for text-independent speech segmentation is proposed. The novelty consists in a preprocessing based on critical-band perceptual analysis and an original algorithm for the individuation of phoneme boundaries. The results are promising since the method gives 74% of correct segmenta-tion without presenting over-segmentation.
This paper compares three unsupervised projection methods: Principal Component Analysis (PCA), which is linear, Self-Organizing Map (SOM) and Curvilinear Component Analysis (CCA), which are both nonlinear. Performance comparison of the three methods is made on a set of seismic data recorded on Stromboli that includes three classes of signals:… (More)
– This paper reports on the unsupervised analysis of seismic signals recorded by four stations situated on the Vesuvius area in Naples, Italy. The dataset under examination is composed of earthquakes and false events like thunders, quarry blasts and man-made undersea explosions. The goal is to use these specific data for comparing the performance of three… (More)
Human beings seem to be able to recognize emotions from speech very well and information communication technology aims to implement machines and agents that can do the same. However, to be able to automatically recognize affective states from speech signals, it is necessary to solve two main technological problems. The former concerns the identification of… (More)