A Perspective Study on Speech Emotion Recognition: Databases, Features and Classification Models

Automatic Speech Recognition (ASR) is a popular research area with many variations in human behaviour functionalities and interactions. Human beings want speech for communication and Conversations. When the conversation is going on, the information or message of the speech utterances is transferred. It also consists of message which includes speaker’s traits like emotion, his or her physiological characteristics and environmental statistics. There is a tremendous number of signals or records that are complex and encoded, but these can be decoded quickly because of human intelligence. Many academics in the domain of Human Computer Interaction (HCI) are working to automate speech generation and the extraction of speech attributes and meaning. For example, ASR can regulate the usage of voice command and maintain dictation discipline while also recognizing and verifying the speech of the speaker. As a result of accent and nativity traits, the speaker's emotional state can be discerned from the speech. In this Paper, we discussed Speech Production System of Human, Research Problems in Speech Processing, SER system Motivation, Challenges and Objectives of Speech Emotion Recognition, so far the work done on Telugu Speech Emotion Databases and their role thoroughly explained. In this Paper, our own Created Database i.e., (DETL) Database for Emotions in Telugu Language and the software Audacity for creating that database is discussed clearly.

Effect of spatiotemporally changing environment on serial dependence in ensemble representations

The recent visual past has a strong impact on our current perception. Recent studies of serial dependence in perception show that low-level adaptation repels our current perception away from previous stimuli whereas post-perceptual decision attracts perceptual report toward the immediate past. In their studies, these repulsive and attractive biases were observed with different task demands perturbing ongoing sequential process. Therefore, it is unclear whether the opposite biases arise naturally in navigating complex real-life environments. Here we only manipulated the environmental statistics to characterize how serially dependent perceptual decisions unfold in spatiotemporally changing visual environments. During sequential mean orientation adjustment task on the array of Gabor patches, we found that the repulsion effect dominated only when ensemble variance increased across consecutive trials whereas the attraction effect prevailed when ensemble variance decreased or remained the same. The observed attractive bias by high-to-low-variance stimuli and repulsive bias by low-to-high-variance stimuli were reinforced by the repeated exposure to the low and the high ensemble variance, respectively. Further, this variance-dependent differential pattern of serial dependence in ensemble representation remained the same regardless of whether observers had a prior knowledge of environmental statistics or not. We used a Bayesian observer model constrained by visual adaptation to provide a unifying account of both attractive and repulsive bias in perception. Our results establish that the temporal integration and segregation of visual information is flexibly adjusted through variance adaptation.

The impacts of economic freedom on the environment: The case of carbon dioxide emissions in seven ASEAN countries

Global warming has been acknowledged as one of the main environmental issues, and economic freedom as one of the institutional factors is believed to be the key to protecting the environment. This study evaluates the proposition that countries with higher economic freedom will have a better environment than countries with a dictatorship government. This study uses panel data regression data from 2011 to 2017 and consists of seven ASEAN Countries. This study constructs an econometrics model with three main variables, i.e., economic freedom, Information, and Communication Technology (ICT), and real gross domestic products (GRDP) that have an impact on carbon dioxide (CO2) emissions. The main results show that (1) higher economic freedom leads to low CO2 emissions. (2) better Information and Communication Technology reduces the level of CO2 emissions. And (3) Positive economic growth influence a higher level of CO2 emissions. The policy implication implies that the governments in seven ASEAN countries should support more economic freedom to support a better environment, use efficient ITC that leads to the protection of the environment. Apply the environmental statistics into the calculation of GRDP to improve countries’ capacities to manage their economies and natural resources.

Information support of nature management and environmental protection in Ukraine

This research develops the problem of effectiveness of the management system and protection of the natural environment. In this research, the various factors of management activity are proposed. To achieve this goal we conducted research on information and analytical systems, which are implemented in the management of nature and environmental protection of Ukraine at three levels: regional (oblast), district and urban. The article presents the results of the analysis of classifiers for systematization of circulating documents, functions of computer document management systems, including those implemented in the management of nature and environmental protection, as well as the level of satisfaction with the quality of software products used to management. It is established that for information support of management activities the most necessary are databases on financial and economic activities, regulatory, personnel, resource, logistics and environmental statistics. The importance of information flow system analysis and information technologies are constantly developing and adapting to user awareness. In the investigation underlined the practical use of different methods to solve similar problems taking into account the peculiarities of the organization.

Agreement errors are predicted by rational inference in sentence processing

People are able to understand language in challenging settings which often require them to correct for speaker errors, environmental noise, and perceptual unreliability. To account for these abilities, it has recently been proposed that people are adapted to correct for noise during language comprehension, via rational Bayesian inference. In the present research, we demonstrate that a rational noisy-channel framework for sentence comprehension can account for a well-known phenomenon—subject-verb agreement errors (e.g. The key to the cabinets are…). A series of experiments provides evidence that: a) agreement errors are associated with misrepresentations of the sentence consistent with noisy-channel inferences and b) agreement errors are rationally sensitive to environmental statistics and properties of the noise. These findings support the hypothesis that agreement errors in production result in part from a sentence comprehension mechanism that is adapted to understanding language in noisy environments.

Hippocampal representations switch from errors to predictions during acquisition of predictive associations

We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N=24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we found that the hippocampus initially preferentially represented unexpected shapes (i.e., those that violated the cue regularities), but later switched to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus in involved both acquiring and exploiting predictive associations, and switches between these modes depending on whether learning is ongoing or complete.

Development of Automated Environmental Data Collection System and Environment Statistics Dashboard

Environmental data such as pollutants, temperature, and humidity are data that have a role in the agricultural sector in predicting rainfall conditions. In fact, pollutant data is common to be used as a proxy to see the density of industry and transportation. With this need, it is necessary to have automated data from outside websites that are able to provide data faster than satellite confirmation. Data sourced from IQair, can be used as a benchmark or confirmative data for weather and environmental statistics in Indonesia. Data is taken by scraping method on the website. Scraping is done on the API available on the website. Scraping is divided into 2 stages, the first is to determine the location in Indonesia, the second is to collect statistics such as temperature, humidity, and pollutant data (AQI). The module used in python is the scrapy module, where the crawling is effective starting from May 2020. The data is recorded every three hours for all regions of Indonesia and directly displayed by the Power BI-based dashboard. We also illustrated that AQI data can be used as a proxy for socio-economic activity and also as an indicator in monitoring green growth in Indonesia.

Spatial structure, phase, and the contrast of natural images

The sensitivity of the human visual system is thought to be shaped by environmental statistics. A major endeavour in visual neuroscience, therefore, is to uncover the image statistics that predict perceptual and cognitive function. When searching for targets in natural images, for example, it has recently been proposed that target detection is inversely related to the spatial similarity of the target to its local background. We tested this hypothesis by measuring observers' sensitivity to targets that were blended with natural image backgrounds. Importantly, targets were designed to have a spatial structure that was either similar or dissimilar to the background. Contrary to masking from similarity, however, we found that observers were most sensitive to targets that were most similar to their backgrounds. We hypothesised that a coincidence of phase-alignment between target and background results in a local contrast signal that facilitates detection when target-background similarity is high. We confirmed this prediction in a second experiment. Indeed, we show that, by solely manipulating the phase of a target relative to its background, the target can be rendered easily visible or completely undetectable. Our study thus reveals a set of image statistics that predict how well people can perform the ubiquitous task of detecting an object in clutter.

Predicting Land Use Changes within the Tazekka Park and Its Borders via a Cellular Automata-Markov Modeling of Satellite Images

This article describes a new approach of land cover study to predicting and combatting deforestation based on satellite imagery as environmental statistics. Specifically, a stochastic mathematical cellular automata-Markov model was used to predict land-use changes in the Tazekka Park and its borders in TAZA province in Morocco. The model was used mainly to create thematic forecast maps. Through the proposed approach, we derived data and statistics covering the period 2000 to 2020 and then constructed a predictive map for the year 2040 using ArcGIS 10.4. The evaluation of our model’s effectiveness was confirmed by calculating the Markov transition matrix in the derivation of the final map. These results can improve the management of forest areas and serve as a reference in addressing the direct effects of forests on the environment.

Addressing Missing Environmental Data via a Machine Learning Scheme

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air-quality-monitoring networks that operate in urban areas provide data on the most important pollutants, which, via environmental statistics, can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air-quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air-quality distributions. This study used point-monitoring data from the network of stations that operates in Athens, Greece. A machine-learning scheme was applied as a method to spatially estimate pollutants’ concentrations, and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes.