Analyzing the interaction between the reader's voice and the linguistic structure of the text: a preliminary study

In this study, we present a preliminary analysis of the relationship between the linguistic profile of a text and the voice properties of the reader aiming to improve the speech-based emotion recognition systems. To this aim, we recorded the speech signals from a group of 32 healthy volunteers reading aloud neutral and affective texts and used the BioVoice toolbox to compute some of the main speech features. The selected texts were analyzed to quantify their lexical, morpho-syntactic, and syntactic content. Correlation and Support Vector Regressor analyses between linguistic and speech features have shown a significant modulation of some voice acoustic properties performed by the linguistic structure of the text. Particularly, a significant effect was shown on some specific speech features often used for the assessment of human emotional state (e.g., F0). This suggests that the lexical, morpho-syntactic, and syntactic properties could play an important role in the emotional dynamics of a person

Internet of Things Enabled Technologies for Behaviour Analytics in Elderly Person Care: A Survey

The advances in sensor technology over recent years has provided new ways for researchers to monitor the elderly in uncontrolled environments. Sensors have become smaller, cheaper and can be worn on the body, potentially creating a network of sensors. Smart phones are also more common in the average household and can also provide some behavioural analysis due to the built in sensors. As a result of this, researchers are able to monitor behaviours in a more natural setting, which can lead to more useful data. This is important for those that may be suffering from mental illness as it allows for continuous, non-invasive monitoring in order to diagnose symptoms from different behaviours. However there are various challenges that need to be addressed ranging from issues with sensors to the involvement of human factors. It is vital that these challenges are taken into consideration along with the major behavioural symptoms that can appear in an Elderly Person. For a person suffering with Dementia, the application of sensor technologies can improve the quality of life of the person and also monitor the progress of the disease through behavioural analysis. This paper will consider the behaviours that can be associated with dementia and how these behaviours can be monitored through sensor technology. We will also provide an insight into some sensors and algorithms gathered through survey in order to provide advantages and disadvantages of these technologies as well as to present any challenges that may face future research

Voice analysis for neurological disorder recognition – a systematic review and perspective on emerging trends

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance

Stress State Evaluation by an Improved Support Vector Machine

Effective methods of evaluation of the psychological pressure can detect and assess realtime stress states, warning people to pay necessary attention to their health. This study is focused on the stress assessment issue using an improved support vector machine (SVM) algorithm on the base of surface electromyographic signals. After the samples were clustered, the cluster results were given to the loss function of the SVM to screen training samples. With the imbalance amongst the training samples after screening, a weight was given to the loss function to reduce the prediction tendentiousness of the classifier and, therefore, to decrease the error of the training sample and make up for the influence of the unbalanced samples. This improved the algorithm, increased the classification accuracy from 73.79% to 81.38%, and reduced the running time from 1973.1 to 540.2 sec. Experimental results show that this algorithm can help to effectively avoid the influence of individual differences on a stress appraisal effect and to reduce the computational complexity during the training phase of the classifierЕфективні методи визначення ступеня психологічного тиску можуть забезпечувати виявлення та оцінку стресових станів у реальному часі, примушуючи людей приділяти необхідну увагу їх здоров’ю. Метою нашого дослідження було оцінити стан стресу з використанням покращеного методу опорних векторів (SVM), базуючись на відведенні поверхневих електроміограм. Після того, як зразки даних були кластеризовані, результати передавалися до функції розділення SVM для того, щоб представити тренувальні зразки. Після встановлення дисбалансу між тренувальними зразками після скринінга для функції розділення надавався параметр ваги для зменшення тенденційності прогнозування класифікатора і, таким чином, зменшення похибки тренувального зразка і впливу незбалансованих зразків. Це покращувало алгоритм, підвищувало точність класифікації від 73.79 до 81.38 % та зменшувало час обробки від 1973.1 до 540.2 с. Результати експериментів показали, що даний алгоритм може допомогти ефективно уникнути впливу індивідуальних відмінностей на оцінювання стресу та зменшити складність комп’ютерних розрахунків у перебігу тренувальної фази діяльності класифікатора

Force-Velocity Assessment of Caress-Like Stimuli Through the Electrodermal Activity Processing: Advantages of a Convex Optimization Approach

We propose the use of the convex optimization-based EDA (cvxEDA) framework to automatically characterize the force and velocity of caressing stimuli through the analysis of the electrodermal activity (EDA). CvxEDA, in fact, solves a convex optimization problem that always guarantees the globally optimal solution. We show that this approach is especially suitable for the implementation in wearable monitoring systems, being more computationally efficient than a widely used EDA processing algorithm. In addition, it ensures low-memory consumption, due to a sparse representation of the EDA phasic components. EDA recordings were gathered from 32 healthy subjects (16 females) who participated in an experiment where a fabric-based wearable haptic system conveyed them caress-like stimuli by means of two motors. Six types of stimuli (combining three levels of velocity and two of force) were randomly administered over time. Performance was evaluated in terms of execution time of the algorithm, memory usage, and statistical significance in discerning the affective stimuli along force and velocity dimensions. Experimental results revealed good performance of cvxEDA model for all of the considered metrics

DEMoS: an Italian emotional speech corpus - elicitation methods, machine learning, and perception

DEMoS (Database of Elicited Mood in Speech), is a corpus of induced emotional speech in Italian. DEMoS encompasses 9,365 emotional and 332 neutral samples produced by 68 native speakers (23 females, 45 males) in seven emotional states: the ‘big six’ anger, sadness, happiness, fear, surprise, disgust, and the secondary emotion guilt. To get more realistic productions, instead of acted speech, DEMoS contains emotional speech elicited by combinations of Mood Induction Procedures (MIP). Three elicitation methods are presented, made up by the combination of at least three MIPs, and considering six different MIPs in total. To select samples ‘typical’ of each emotion, evaluation strategies based on self- and external assessment were applied. The selected part of the corpus encompasses 1,564 prototypical samples produced by 59 speakers (21 females, 38 male). DEMoS has been published in the Journal Language, Resousrces, and Evalaution. Emilia Parada-Cabaleiro, Giovanni Costantini, Anton Batliner, Maximilian Schmitt, and Björn Schuller (2019), DEMoS: An Italian emotional speech corpus. Elicitation methods, machine learning, and perception, Language, Resources, and Evaluation, Feb 2019. https://rdcu.be/bn7o

Recognition of Emotions in Mexican Spanish Speech: An Approach Based on Acoustic Modelling of Emotion-Specific Vowels

An approach for the recognition of emotions in speech is presented. The target language is Mexican Spanish, and for this purpose a speech database was created. The approach consists in the phoneme acoustic modelling of emotion-specific vowels. For this, a standard phoneme-based Automatic Speech Recognition (ASR) system was built with Hidden Markov Models (HMMs), where different phoneme HMMs were built for the consonants and emotion-specific vowels associated with four emotional states (anger, happiness, neutral, sadness). Then, estimation of the emotional state from a spoken sentence is performed by counting the number of emotion-specific vowels found in the ASR’s output for the sentence. With this approach, accuracy of 87–100% was achieved for the recognition of emotional state of Mexican Spanish speech

Methods to detect and reduce driver stress: a review

Automobiles are the most common modes of transportation in urban areas. An alert mind is a prerequisite while driving to avoid tragic accidents; however, driver stress can lead to faulty decision-making and cause severe injuries. Therefore, numerous techniques and systems have been proposed and implemented to subdue negative emotions and improve the driving experience. Studies show that conditions such as the road, state of the vehicle, weather, as well as the driver’s personality, and presence of passengers can affect driver stress. All the above-mentioned factors significantly influence a driver’s attention. This paper presents a detailed review of techniques proposed to reduce and recover from driving stress. These technologies can be divided into three categories: notification alert, driver assistance systems, and environmental soothing. Notification alert systems enhance the driving experience by strengthening the driver’s awareness of his/her physiological condition, and thereby aid in avoiding accidents. Driver assistance systems assist and provide the driver with directions during difficult driving circumstances. The environmental soothing technique helps in relieving driver stress caused by changes in the environment. Furthermore, driving maneuvers, driver stress detection, driver stress, and its factors are discussed and reviewed to facilitate a better understanding of the topic