Automatic prosodic analysis for computer aided pronunciation teaching

Correct pronunciation of spoken language requires the appropriate modulation of acoustic characteristics of speech to convey linguistic information at a suprasegmental level. Such prosodic modulation is a key aspect of spoken language and is an important component of foreign language learning, for purposes of both comprehension and intelligibility. Computer aided pronunciation teaching involves automatic analysis of the speech of a non-native talker in order to provide a diagnosis of the learner's performance in comparison with the speech of a native talker. This thesis describes research undertaken to automatically analyse the prosodic aspects of speech for computer aided pronunciation teaching. It is necessary to describe the suprasegmental composition of a learner's speech in order to characterise significant deviations from a native-like prosody, and to offer some kind of corrective diagnosis. Phonological theories of prosody aim to describe the suprasegmental composition of speech..

Computer assisted enhanced volumetric segmentation magnetic imaging data using a mixture of artificial neural networks

An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. The method was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T(2) weighted) and from a clinical study of human Alzheimer's disease (T(1) weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi-automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88

VOLATILITY MODELS AND THEIR APPLICATION TO OPTIONS PRICING AND RISK MANAGEMENT.

We look at various volatility models and their applications. Starting from a basic linear GARCH model we proceed to more advanced linear GARCH models involving leverage effects and asymmetry. We also look at some examples of non-linear GARCH models such as TGARCH, smooth transition GARCH and NNGARCH. ML estimation technique is considered. Some applications to options pricing and risk management are presented. Next we turn our attention to discrete and continuous stochastic volatility models. Filtering techniques such as Kalman filter, particle filter are presented and estimation approaches based on filtering as well as efficient method of moments are elaborated on in details. Finally we take a look at the implied volatility surface and some ways of its estimation

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Heart-brain synchronization breakdown in Parkinson's disease

Heart rate variability (HRV) abnormalities are potential early biomarkers in Parkinson's disease (PD) but their relationship with central autonomic network (CAN) activity is not fully understood. We analyzed the synchronization between HRV and brain activity in 31 PD patients and 21 age-matched healthy controls using blood-oxygen-level-dependent (BOLD) signals from resting-state functional brain MRI and HRV metrics from finger plethysmography recorded for 7.40 min. We additionally quantified autonomic symptoms (SCOPA-AUT) and objective autonomic cardiovascular parameters (blood pressure and heart rate) during deep breathing, Valsalva, and head-up tilt, which were used to classify the clinical severity of dysautonomia. We evaluated HRV and BOLD signals synchronization (HRV-BOLD-sync) with Pearson lagged cross-correlations and Fisher's statistics for combining window-length-dependent HRV-BOLD-Sync Maps and assessed their association with clinical dysautonomia. HRV-BOLD-sync was lower significantly in PD than in controls in various brain regions within CAN or in networks involved in autonomic modulation. Moreover, heart-brain synchronization index (HBSI), which quantifies heart-brain synchronization at a single-subject level, showed an inverse exposure-response relationship with dysautonomia severity, finding the lowest HBSI in patients with severe dysautonomia, followed by moderate, mild, and, lastly, controls. Importantly, HBSI was associated in PD, but not in controls, with Valsalva pressure recovery time (sympathetic), deep breathing E/I ratio (cardiovagal), and SCOPA-AUT. Our findings support the existence of heart-brain de-synchronization in PD with an impact on clinically relevant autonomic outcomes.We want to thank all the patients and participants involved in the study. This study was partially co-funded by Michael J. Fox Foundation [RRIA 2014 (Rapid Response Innovation Awards) Program (Grant ID: 10189)], by the Carlos III Health Institute, and the European Union (ERDF/ESF, "A Way to Make Europe"/"Investing in Your Future") through the projects PI14/00679 and PI16/00005, the Juan Rodes grant "JR15/00008" (I.G.), and by the Department of Health of the Basque Government through the project "2016111009" and "2020333033". A.J.M. was supported by a predoctoral grant from the Basque Government (PRE_2019_1_0070). M.I. acknowledges financial support from"La Caixa" Foundation (ID 100010434, fellowship LCF/BQ/EU20/11810065). The Edmond and Lily Safra Center for Brain Sciences and the Basque Government (POS_2019_2_0020) to A.E. J.M.C. is funded by Ikerbasque: The Basque Foundation for Science and from the Ministerial de Economia, Industria y Competitividad (Spain) and FEDER (grant DPI2016-79874-R), and from the Department of Economic and Infrastructure Development of the Basque Country (Elkartek Program, KK-2018/00032, KK-2018/00090, and KK-2021/00009/BCB)