Speech Based Machine Learning Models for Emotional State Recognition and PTSD Detection

Recognition of emotional state and diagnosis of trauma related illnesses such as posttraumatic stress disorder (PTSD) using speech signals have been active research topics over the past decade. A typical emotion recognition system consists of three components: speech segmentation, feature extraction and emotion identification. Various speech features have been developed for emotional state recognition which can be divided into three categories, namely, excitation, vocal tract and prosodic. However, the capabilities of different feature categories and advanced machine learning techniques have not been fully explored for emotion recognition and PTSD diagnosis. For PTSD assessment, clinical diagnosis through structured interviews is a widely accepted means of diagnosis, but patients are often embarrassed to get diagnosed at clinics. The speech signal based system is a recently developed alternative. Unfortunately,PTSD speech corpora are limited in size which presents difficulties in training complex diagnostic models. This dissertation proposed sparse coding methods and deep belief network models for emotional state identification and PTSD diagnosis. It also includes an additional transfer learning strategy for PTSD diagnosis. Deep belief networks are complex models that cannot work with small data like the PTSD speech database. Thus, a transfer learning strategy was adopted to mitigate the small data problem. Transfer learning aims to extract knowledge from one or more source tasks and apply the knowledge to a target task with the intention of improving the learning. It has proved to be useful when the target task has limited high quality training data. We evaluated the proposed methods on the speech under simulated and actual stress database (SUSAS) for emotional state recognition and on two PTSD speech databases for PTSD diagnosis. Experimental results and statistical tests showed that the proposed models outperformed most state-of-the-art methods in the literature and are potentially efficient models for emotional state recognition and PTSD diagnosis

Plotting Time: Exploring Visual Representations for Time Series Classification

Tese de mestrado, Engenharia Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasTime series data is a collection of data points acquired in successive order over a period of time, allowing us to obtain temporal information and make time-based predictions through the combination of Machine Learning (ML) algorithms. Time series are prevalent in crucial sectors for society’s development, such as Economy, Health, Weather, and Astronomy, with the objective of improving the quality of life through the prediction of climate changes, economic variations, earthquakes, and other types of events. These sectors require models with good predictive abilities and capable of scaling as the volume of data gradually increases. We can address this issue by using Deep Learning (DL) models that can keep a good performance while increasing the amount of data. One example is the Convolutional Neural Network (CNN), which uses images as input in several activity sectors. There is not much time series-related work with deep learning models and image generation. As a result, our objective is to develop new methods for image generation and then train them with a simple CNN. We focus on time series data to create a new algorithm for converting non-image time series data into graphical images that contain either box plots or violin plots with statistical information. We hypothesize that CNNs can interpret and learn different elements of the plots, and by comparing two different approaches, we can verify this statement. Our results indicate that CNNs may not understand some elements of the box and violin plots, for example, the outliers and quartiles, and focus more on the density and distribution of the data. In the future, it would be interesting to study alternative image generation algorithms and explore graphical representations in multivariate datasets

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach