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Auto-Associative Initialization of LSTM Neural Networks for Fundamental Frequency Detection in Noisy Speech Signals
(2018)
In this paper, we present a new approach for fundamental frequency detection in noisy speech, based on Long Short-term Memory Neural Networks (LSTM). Fundamental frequency is one of the most important parameters of human ...
Robustness of LSTM neural networks for the enhancement of spectral parameters in noisy speech signals
(2019)
In this paper, we carry out a comparative performance analysis of Long Short-term Memory (LSTM) Neural Networks for the task of noise reduction. Recent work in this area has shown the advantages of this kind of network for ...
Estado del arte de la predicción de variables en sistemas de Ingeniería Eléctrica basada en inteligencia artificial
State of the art of predicting Electrical Engineering variables based on artificial intelligence
(2021-11)
Existe una gran cantidad de sistemas que se estudian y desarrollan en el campo de la Ingeniería Eléctrica en los que se realizan análisis que tienen como uno de sus fines principales la predicción de sus variables, tanto ...
Hidden Markov Models for artificial voice production and accent modification
(2016)
In this paper, we consider the problem of accent modification between Castilian Spanish and Mexican Spanish. This is an interesting application area for tasks such as the automatic dubbing of pictures and videos with ...
Assessing the robustness of recurrent neural networks to enhance the spectrum of reverberated speech
(2020)
Implementing voice recognition systems and voice analysis in real-life contexts present important challenges, especially when signal recording/registering conditions are adverse. One of the conditions that produce signal ...
Reconstructing fundamental frequency from noisy speech using initialized autoencoders
(2020-10)
In this paper, we present a new approach for fundamental frequency (f0) detection in noisy speech, based on Long Short-term Memory Neural Networks (LSTM). f0 is one of the most important parameters of human speech. Its ...
An experimental study on fundamental frequency detection in reverberated speech with pre-trained recurrent neural networks
(2020)
The detection of the fundamental frequency (f0) in speech signals is relevant in areas such as automatic speech recognition and identification, with multiple potential applications. For example, in virtual assistants, ...
A first approach to acoustic characterization of Costa Rican children’s speech
Un primer acercamiento a la caracterización acústica del habla de niños costarricenses
(2020-03-27)
As human interaction with computers becomes more pervasive, the value of developing automatic speech recognition, text-to-speech synthesis, and related speech technologies become more important for people of all ages, ...
Primeras experiencias de colaboración docente entre las carreras de Ingeniería Eléctrica y Educación Especial de la Universidad de Costa Rica, en torno a proyectos de acción social a favor de la población en condición de discapacidad
Opportunities for collaboration between Electrical Engineering and Special Education majors at the University of Costa Rica: Social outreach projects to benefit disabled population groups
(2021-10)
En el presente artículo se exponen y contextualizan las experiencias de vinculación docente entre las carreras de Educación Especial e Ingeniería Eléctrica en la Universidad de Costa Rica durante el periodo de 2018 a 2020, ...
Descubrimiento del estilo de aprendizaje dominante en estudiantes de Matemática superior
Discovering dominant learning styles in higher level Math students
(2020)
La presente investigación se desarrolló con el objetivo de descubrir cuáles son los estilos de aprendizaje dominantes en estudiantes de Matemática Superior de la carrera de Ingeniería Eléctrica en la Universidad de Costa ...