Abstract:A frequently used procedure to examine the relationship between categorical and dimensional descriptions of emotions is to ask subjects to place verbal expressions representing emotions in a continuous multidimensional emotional space. This work chooses a different approach. It aims at creating a system predicting the values of Activation and Valence (AV) directly from the sound of emotional speech utterances without the use of its semantic content or any other additional information. The system uses X-vectors… Show more
“…However, the provided dataset for was not appropriate for this setup. The emotions and stress in the audio speech conversation utterances [14], [15] will be investigated in the future work.…”
An effective contact tracking strategy helps to maintain control over the Covid-19 pandemic. People without visible symptoms make it a complex problem because there has to be an unobtrusive way to discover that they are virus carriers and have to be isolated. Automated Covid-19 respiratory symptoms analysis helps to focus on people with respiratory symptoms. In our approach, a telephone call system leads a dialog and discovers Covid-19 disease by analyzing a person's speech and cough. After a positive match, it invites the person to PCR testing to confirm or reject the diagnosis. We compare our speech and cough detection system with Interspeech Computational Paralinguistics Challenge (ComParE) 2021. The results of Covid-19 Speech Sub-Challenge sub-challenge (CSS) show that we outperform the baseline results by 4.3% of the Unweighted Average Recall value.
“…However, the provided dataset for was not appropriate for this setup. The emotions and stress in the audio speech conversation utterances [14], [15] will be investigated in the future work.…”
An effective contact tracking strategy helps to maintain control over the Covid-19 pandemic. People without visible symptoms make it a complex problem because there has to be an unobtrusive way to discover that they are virus carriers and have to be isolated. Automated Covid-19 respiratory symptoms analysis helps to focus on people with respiratory symptoms. In our approach, a telephone call system leads a dialog and discovers Covid-19 disease by analyzing a person's speech and cough. After a positive match, it invites the person to PCR testing to confirm or reject the diagnosis. We compare our speech and cough detection system with Interspeech Computational Paralinguistics Challenge (ComParE) 2021. The results of Covid-19 Speech Sub-Challenge sub-challenge (CSS) show that we outperform the baseline results by 4.3% of the Unweighted Average Recall value.
“…By employing this rescaled range, the proposed approach aims at creating a standardized framework where the origin accurately represents a state of neutrality or absence of emotional intensity. The neutral discrete affective state is more efficiently mapped by a cluster of points with the center in the origin coordinates (0, 0), rather than by a single unique point 37 . Figure 2 Valence-arousal plane divided into 5 quadrants: HAHV, HALV, LALV, LAHV, and N. The neutral region N is located in the center of the valence-arousal plane.…”
Affective states are psycho-physiological constructs connecting mental and physiological processes. They can be represented in terms of arousal and valence according to the Russel’s model and can be extracted from physiological changes in human body. However, a well-established optimal feature set and a classification method effective in terms of accuracy and estimation time are not present in the literature. This paper aims at defining a reliable and efficient approach for real-time affective state estimation. To obtain this, the optimal physiological feature set and the most effective machine learning algorithm, to cope with binary as well as multi-class classification problems, were identified. ReliefF feature selection algorithm was implemented to define a reduced optimal feature set. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis, were implemented to compare their effectiveness in affective state estimation. The developed approach was tested on physiological signals acquired on 20 healthy volunteers during the administration of images, belonging to the International Affective Picture System, conceived for inducing different affective states. ReliefF algorithm reduced the number of physiological features from 23 to 13. The performances of machine learning algorithms were compared and the experimental results showed that both accuracy and estimation time benefited from the optimal feature set use. Furthermore, the KNN algorithm resulted to be the most suitable for affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.
“…In contrast to chronic stress, our present work focuses on the acute, or momentary stress that is experienced as an immediate perceived threat, either physical, emotional, or psychological [7]. Epel notes that "Acute psychological stress responses are often measured by capturing specific emotional states.…”
The term stress can designate a number of situations and affective reactions. This work focuses on the immediate stress reaction caused by, for example, threat, danger, fear, or great concern. Could measuring stress from speech be a viable fast and non-invasive method? The article describes the development of a system predicting stress from voice -from the creation of the database, and preparation of the training data to the design and tests of the regressor. StressDat, an acted database of speech under stress in Slovak, was designed. After publishing the methodology during its development in [1], this work describes the final form, annotation, and basic acoustic analyses of the data. The utterances presenting various stress-inducing scenarios were acted at three intended stress levels. The annotators used a "stress thermometer" to rate the perceived stress in the utterance on a scale from 0 to 100. Thus, data with a resolution suitable for training the regressor was obtained. Several regressors were trained, tested and compared. On the test-set, the stress estimation works well (R square = 0.72, Concordance Correlation Coefficient = 0.83) but practical application will require much larger volumes of specific training data. StressDat was made publicly available.
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