Monte Carlo simulation of confined water

Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2016, Tutor: Giancarlo FranzeseIn living organisms and many applications water is nanoconfined. Here we study water confined between hydrophobic parallel walls as a function of the wall-wall separation Lz between 0.6 and 4.8 nm. We calculate response functions and density by Monte Carlo simulations at different temperatures and pressures of a many-body coarse-grained model of water that has been studied in previous works for the case of a single layer. For all the number of layers considered here we always find that water has density anomaly as in experimental bulk water and that it has a critical phase transition between two liquid phases with different structure, density and energy. We find that the phase diagram changes in a continuous way as the number of layers increases, suggesting that the liquid-liquid critical point should occur also in the bulk case. These results shed light onto the debated bulk-water phase diagram and could be relevant in nanotechnology applications and biological system

Correlation of speech/non-speech events with photo-plethysmographic (PPG) signal

Treball fi de màster de: Master in Intelligent Interactive SystemsTutors: Jordi Luque, Mireia FarrúsThe use of photoplethysmogram signal (PPG) for heart monitoring is commonly found nowadays in smartphones and wrist wearables. Besides heart rate or sleep monitoring common usage, it has been proved that information from PPG can be extracted for other uses, like person verification, for example. In this work, we evaluate whether if speech/non-speech events can be inferred from fluctuations they might cause in the pulse signal. In order to do so, an exploration on end-to-end convolutional neural network architectures is done for performing both feature extraction and classification of the mentioned events. The results are motivating, detecting speech in PPG signal with a 68.2% AUC using the best performing architecture. On the other hand, a first experiment on speaker’s voice pitch detection is done, in order to check if a prosody marker such as pitch variation could be present in PPGs, but such clue is not clearly found in the results obtained. Nevertheless, the correlation between speech and PPG signal is proven and the way is paved for further experiments on this topic

Data augmentation for low-resource Quechua ASR improvement

Comunicació presentada a INTERSPEECH 2022, celebrat del 18 al 22 de setembre de 2022 a Inchon, Corea del Sud.Automatic Speech Recognition (ASR) is a key element in new services that helps users to interact with an automated system. Deep learning methods have made it possible to deploy systems with word error rates below 5% for ASR of English. However, the use of these methods is only available for languages with hundreds or thousands of hours of audio and their corresponding transcriptions. For the so-called low-resource languages to speed up the availability of resources that can improve the performance of their ASR systems, methods of creating new resources on the basis of existing ones are being investigated. In this paper we describe our data augmentation approach to improve the results of ASR models for low-resource and agglutinative languages. We carry out experiments developing an ASR for Quechua using the wav2letter++ model. We reduced WER by 8.73% through our approach to the base model. The resulting ASR model obtained 22.75% WER and was trained with 99 hours of original resources and 99 hours of synthetic data obtained with a combination of text augmentation and synthetic speech generation.This work has been partially supported by the Project PID2019-104512GB-I00, Ministerio de Ciencia e Innovación and Agencia Estatal de Investigación (Spain), and the INGENIOUS project from the European Union’s Horizon 2020 Research and Innovation Program under grant numbers 833435. The third author has been funded by the Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades and the Fondo Social Europeo (FSE) under grant RYC-2015-17239 (AEI/FSE, UE)