Phoneme and sentence-level ensembles for speech recognition

We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level and one at the utterance level, with a phoneme-level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that the gain of boosting methods compared to a single hidden Markov model is in all cases only marginal, while bagging significantly outperforms all other methods. We thus conclude that bagging methods, which have so far been overlooked in favour of boosting, should be examined more closely as a potentially useful ensemble learning technique for speech recognition

Estudio del diseño de un sistema electrónico para detectar a personas con fiebre mediante el uso de una cámara térmica

Actualmente el mundo atraviesa una de las peores crisis sanitarias de la historia ocasionada por la pandemia del COVID-19, esta enfermedad tiene entre los síntomas más comunes a la fiebre con un 89.7% de incidencia en los infectados, según estudios de la Organización Mundial de la Salud. Una de las medidas principales para evitar los contagios es la detección de personas con fiebre, los cuales son potenciales contagiados y deben ser aislados. La termografía infrarroja es una técnica eficaz para la detección de personas con fiebre debido a que permite obtener información sobre la temperatura de las personas de forma instantánea, sin contacto y conservando el distanciamiento social. El presente trabajo de investigación se centra en el estudio sobre el diseño de un sistema electrónico que permita la detección de personas con fiebre de manera continua, basado en estudios realizados sobre sistemas termográficos que fueron efectivos para frenar la pandemia del SAARS y la gripe AH1N1. Este sistema contempla el uso de la cámara térmica VUMII OFC con la cual se cuenta dentro del laboratorio de sistemas aéreos no tripulados de la PUCP y el uso de una temperatura de referencia externa para la autocalibración del sistema en funcionamiento. Este tipo de arquitectura funciona para sistemas de termografía cuantitativa de bajo coste que no son considerados como equipos médicos, sino son considerados como un sistema de tamizaje.Trabajo de investigació

Ensembles for sequence learning

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement learning problems. In both types of problems, even after having observed a certain amount of data, we are often faced with uncertainty as to which hypothesis is correct among all the possible ones. However, in many methods for both supervised and for reinforcement learning problems this uncertainty is ignored, in the sense that there is a single solution selected out of the whole of the hypothesis space. Apart from the classical solution of analytical Bayesian formulations, ensemble methods offer an alternative approach to representing this uncertainty. This is done simply through maintaining a set of alternative hypotheses. The sequential supervised problem considered is that of automatic speech recognition using hidden Markov models. The application of ensemble methods to the problem represents a challenge in itself, since most such methods can not be readily adapted to sequential learning tasks. This thesis proposes a number of different approaches for applying ensemble methods to speech recognition and develops methods for effective training of phonetic mixtures with or without access to phonetic alignment data. Furthermore, the notion of expected loss is introduced for integrating probabilistic models with the boosting approach. In some cases substantial improvements over the baseline system are obtained. In reinforcement learning problems the goal is to act in such a way as to maximise future reward in a given environment. In such problems uncertainty becomes important since neither the environment nor the distribution of rewards that result from each action are known. This thesis presents novel algorithms for acting nearly optimally under uncertainty based on theoretical considerations. Some ensemble-based representations of uncertainty (including a fully Bayesian model) are developed and tested on a few simple tasks resulting in performance comparable with the state of the art. The thesis also draws some parallels between a proposed representation of uncertainty based on gradient-estimates and on"prioritised sweeping" and between the application of reinforcement learning to controlling an ensemble of classifiers and classical supervised ensemble learning methods

Boosting word error rates

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample error for sentence examples related to the word error rate. Furthermore, for each sentence example we define a probability distribution in time that represents our belief that an error has been made at that particular frame. This is used to weigh the frames of each sentence in the boosting framework. We present preliminary results on the well-known Numbers 95 database that indicate the importance of this temporal probability distribution

Boosting Word Error Rates

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample error for sentence examples related to the word error rate. Furthermore, for each sentence example we define a probability distribution in time that represents our belief that an error has been made at that particular frame. This is used to weigh the frames of each sentence in the boosting framework. We present preliminary results on the well-known Numbers 95 database that indicate the importance of this temporal probability distribution

Boosting Word Error Rates

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample error for sentence examples related to the word error rate. Furthermore, for each sentence example we define a probability distribution in time that represents our belief that an error has been made at that particular frame. This is used to weigh the frames of each sentence in the boosting framework. We present preliminary results on the well-known Numbers 95 database that indicate the importance of this temporal probability distribution