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 to represent sound characteristics of the utterance and Support Vector Regressor for the estimation the AV values. The system is trained on a pool of three publicly available databases with dimensional annotation of emotions. The quality of regression is evaluated on the test sets of the same databases. Mapping of categorical emotions to the dimensional space is tested on another pool of eight categorically annotated databases. The aim of the work was to test whether in each unseen database the predicted values of Valence and Activation will place emotion-tagged utterances in the AV space in accordance with expectations based on Russell’s circumplex model of affective space. Due to the great variability of speech data, clusters of emotions create overlapping clouds. Their average location can be represented by centroids. A hypothesis on the position of these centroids is formulated and evaluated. The system’s ability to separate the emotions is evaluated by measuring the distance of the centroids. It can be concluded that the system works as expected and the positions of the clusters follow the hypothesized rules. Although the variance in individual measurements is still very high and the overlap of emotion clusters is large, it can be stated that the AV coordinates predicted by the system lead to an observable separation of the emotions in accordance with the hypothesis. Knowledge from training databases can therefore be used to predict AV coordinates of unseen data of various origins. This could be used to detect high levels of stress or depression. With the appearance of more dimensionally annotated training data, the systems predicting emotional dimensions from speech sound will become more robust and usable in practical applications in call-centers, avatars, robots, information-providing systems, security applications, and the like.

A new Slovak speech database EWA-DB was created for research purposes aimed at early detection of neurodegenerative diseases from speech. It contains 1649 speakers performing various speech and language tasks, such as sustained vowel phonation, diadochokinesis, naming and picture description. The sample of speakers consists of individuals with Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, and healthy controls. In this article we describe the EWA-DB development process, the language and speech task selection, patient and healthy control recruitment, as well as the testing and recording protocol. The structure and content of the database and file formats are described in detail. We assume that the presented database could be suitable for the development of automatic systems predicting the diagnoses of Alzheimer’s disease, mild cognitive impairment, and Parkinson’s disease from language and speech features.

A new Slovak speech database EWA-DB was created for research purposes aimed at early detection of neurodegenerative diseases from speech. It contains 1649 speakers performing various speech and language tasks, such as sustained vowel phonation, diadochokinesis, naming and picture description. The sample of speakers consists of individuals with Alzheimer’s disease, mild cognitive impairment, Parkinson’s disease, and healthy controls. In this article we describe the EWA-DB development process, the language and speech task selection, patient and healthy control recruitment, as well as the testing and recording protocol. The structure and content of the database and file formats are described in detail. We assume that the presented database could be suitable for the development of automatic systems predicting the diagnoses of Alzheimer’s disease, mild cognitive impairment, and Parkinson’s disease from language and speech features.

The voice communication between pilots and the air traffic controllers is vulnerable to various types of attacks. Speaker verification could be used as an add-on security feature; however, there are several factors that make the use of voice biometry in this scenario difficult to apply. These are among others: open set of speakers, very short utterances, speaker noises, signal clipping, foreign accent of non-native speakers, and high content of background and channel noises in the signal. This paper identifies sources of noise in the entire communication channel and analyzes the influence of these noise components of different types and levels on the reliability of the speaker verification. An i-vector based speaker recognizer with PLDA scoring is used for the experiments. Cockpit noises of several aircrafts and limited-band channel noises are simulated by a software noise-generator. The sensitivity of the speaker verification to the noises of different frequency bands is studied in comparison to the long-term speech spectrum and its variability. Possible measures for increasing the noise robustness of the system are discussed.