We show here a selection of recent AUDIAS publications covering our different research topics and technologies.
Click here to visit the AUDIAS Google Scholar profile for our complete list of publications.
- Calibration of deep probabilistic models with decoupled bayesian neural networks, by J. Maroñas, R. Paredes and D. Ramos, Neurocomputing, Volume 407, 24 September 2020, Pages 194-205.
- Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset, by D. de Benito-Gorron, A. Lozano-Diez, D. T. Toledano and J. González-Rodríguez, EURASIP Journal on Audio, Speech, and Music Processing 2019.1 (2019): 9.
- Deconstructing Cross-Entropy for Probabilistic Binary Classifiers, by D. Ramos, J. Franco-Pedroso, A. Lozano-Diez and J. Gonzalez-Rodriguez, Entropy (ISSN 1099-4300), Vol. 20, n. 3, pp. 208, March 2018.
- Age Estimation in Short Speech Utterances based on LSTM Recurrent Neural Networks, by R. Zazo, P. S. Nidadavolu, N. Chen, J. Gonzalez-Rodriguez and N. Dehak, IEEE Access, March 2018.
- Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation, by J. Tejedor, D.T. Toledano, P. Lopez-Otero, L. Docio-Fernandez, M. Peñagarikano, L.J. Rodriguez-Fuentes and A. Moreno-Sandoval, EURASIP Journal on Audio, Speech, and Music Processing, 2019(1), 13.
- Multi-resolution speech analysis for automatic speech recognition using deep neural networks: Experiments on TIMIT, by D.T. Toledano, M.P. Fernandez-Gallego and A. Lozano-Diez, PLoS ONE, Public Library of Science, Vol. 13, n. 10, e0205355, October 2018.
- A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation, by D. Meuwly, D. Ramos and R. Haraksim, Forensic Science International, Elsevier, Vol. 276, pp. 142-153, July 2017.
- An Analysis of the Influence of Deep Neural Network (DNN) Topology in Bottleneck Feature based Language Recognition, by A. Lozano-Diez, R. Zazo, D. T. Toledano and J. Gonzalez-Rodriguez, PLoS ONE, Public Library of Science, Vol. 12, n. 8, pp. e0182580, August 2017.
- On the use of deep feedforward neural networks for automatic language identification, by I. Lopez-Moreno, J. Gonzalez-Dominguez, D. Martinez, O. Plchot, J. Gonzalez-Rodriguez and P. J. Moreno, Computer Speech and Language, Elsevier, Vol. 40, pp. 46-59, May 2016.
- Linguistically-constrained formant-based i-vectors for automatic speaker recognition, by J. Franco-Pedroso and J. Gonzalez-Rodriguez, Speech Communication, Elsevier Science Publishers B. V., Vol. 76, pp. 61-81, February 2016.
- Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks, by R. Zazo, A. Lozano-Diez, J. Gonzalez-Dominguez, D. T. Toledano and J. Gonzalez-Rodriguez, PloS ONE, Public Library of Science, January 2016.
- Frame-by-frame language identification in short utterances using deep neural networks, by J. Gonzalez-Dominguez, I. Lopez-Moreno, P. J. Moreno and J. Gonzalez-Rodriguez, Neural Networks, Elsevier, Vol. 64, pp. 49-58, September 2014.
- Generating virtual scenarios of multivariate financial data for quantitative trading applications, by J. Franco-Pedroso, J. Gonzalez-Rodriguez, J. Cubero, M. Planas, R. Cobo and F. Pablos, The Journal of Financial Data Science 1 (2), 55-77, 2019.