Catálogo de publicaciones - libros

This paper presents an approach for the automatic speech re-cognition using syllabic units. Its segmentation is based on using the Short-Term Total Energy Function (STTEF) and the Energy Function of the High Frequency (ERO parameter) higher than 3,5 KHz of the speech signal. Training for the classification of the syllables is based on ten related Spanish language rules for syllable splitting. Recognition is based on a Continuous Density Hidden Markov Models and the bigram model language. The approach was tested using two voice corpus of natural speech, one constructed for researching in our laboratory (experimental) and the other one, the corpus Latino40 commonly used in speech researches. The use of ERO and MFCCs parameter increases speech recognition by 5.5% when compared with recognition using STTEF in discontinuous speech and improved more than 2% in continuous speech with three states. When the number of states is incremented to five, the recognition rate is improved proportionally to 98% for the discontinuous speech and to 81% for the continuous one.

We have been building a text-independent speaker recognition system in noisy conditions. In this paper, we propose a novel feature using hybrid PCA/LDA. The feature is created from the convectional MFCC(mel-frequency cepstral coefficients) by transforming them using a matrix. The matrix consists of some components from the PCA and LDA transformation matrices. We tested the new feature using Aurora project Database 2 which is intended for the evaluation of algorithms for front-end feature extraction algorithms in background noise. The proposed method outperformed in all noise types and noise levels. It reduced the relative recognition error by 63.6% than using the baseline feature when the SNR is 15dB.

One of the speaker authentication problems consists on identifying a person only by means of his/her voice. To obtain the best authentication results, it is very important to select the most relevant features from the speech samples, this because we think that not all of the characteristics are relevant for the authentication process and also that many of these data might be redundant. This work presents the design and implementation of a Genetic-Neural algorithm for feature selection used on a speaker authentication task. We extract acoustic features such as Mel Frequency Cepstral Coefficients, on a database composed by 150 recorded voice samples, and a genetic feature selection system combined with a time delay feed-forward neural network trained by scaled conjugate gradient back propagation, to classify/authenticate the speaker. We also show that after the hybrid system finds the best solution, it almost never looses it, even when the search space changes. The design and implementation process, the performed experiments, as well as some results are shown.

This research shows the improvement obtained by including the principal component analysis as part of the feature production in the generation of a speech key. The main architecture includes an automatic segmentation of speech and a classifier. The first one, by using a forced alignment configuration, computes a set of primary features, obtains a phonetic acoustic model, and finds the beginnings and ends of the phones in each utterance. The primary features are then transformed according to both the phone model parameters and the phones segments per utterance. Before feeding these processed features to the classifier, the principal component analysis algorithm is applied to the data and a new set of secondary features is built. Then a support vector machine classifier generates an hyperplane that is capable to produce a phone key. Finally, by performing a phone spotting technique, the key is hardened. In this research the results for 10, 20 and 30 users are given using the YOHO database. 90% accuracy.

In this paper, we introduce a real-time temporal probabilistic knowledge logic, called , which can express not only real-time temporal and probabilistic epistemic properties but also cooperation properties. It is showed that temporal modalities such as “always in an interval”, “until in an interval”, and knowledge modalities such as “knowledge in an interval”, “common knowledge in an interval” and “probabilistic common knowledge” can be expressed in such a logic. The model checking algorithm is given and a case is studied.

This paper extends the scope of the model of regulation of social exchanges based on the concept of a supervisor of social equilibrium. We allow the supervisor to interact with personality-based agents that control the supervisor access to their internal states, behaving either as transparent agents (agents that allow full external access to their internal states) or as non-transparent agents (agents that restrict such external access). The agents may have different personality traits, which induce different attitudes towards both the regulation mechanism and the possible profits of social exchanges. Also, these personality traits influence the agents’ evaluation of their current status. To be able to reason about the social exchanges among personality-based non-transparent agents, the equilibrium supervisor models the system as a Hidden Markov Model.

This paper is a proof of concept report for a bottom-up approach to a conceptual and engineering framework to enable Intelligent Organizations using MAS Technology. We discuss our experience of implementing different types of server agents and a rudimentary for two industrial-scale information systems now in operation. These server agents govern knowledge repositories and user interactions according to workflow scripts that are interpreted by the organization engine. These results show how we have implemented the bottom layer of the proposed framework architecture. They also allow us to discuss how we intend to extend the current organization engine to deal with institutional aspects of an organization other than workflows.

The paper proposes a modeling methodology allowing the specification of multi mobile agent systems using nLNS, a multi level Petri net based formalism. The prey-predator problem is addressed and a modular and hierarchical model for this case study is developed. An overview of a nLNS simulator is presented through the prey predator problem.

In this paper we introduce a technique for runtime fault detection and localization in component-oriented software systems. Our approach allows for the definition of arbitrary properties at the component level. By monitoring the software system at runtime we can detect violations of these properties and, most notably, also locate possible causes for specific property violation(s). Relying on the model-based diagnosis paradigm, our fault localization technique is able to deal with intermittent fault symptoms and it allows for measurement selection. Finally, we discuss results obtained from our most recent case studies.

We present a method for sensor-based exploration of unknown environments by mobile robots. This method proceeds by building a data structure called SRT (Sensor-based Random Tree). The SRT represents a roadmap of the explored area with an associated safe region, and estimates the free space as perceived by the robot during the exploration. The original work proposed in [1] presents two techniques: SRT-Ball and SRT-Star. In this paper, we propose an alternative strategy called SRT-Radial that deals with non-holonomic constraints using two alternative planners named SRT_Extensive and SRT_Goal. We present experimental results to show the performance of the SRT-Radial and both planners.