Разработка алгоритма распознавания эмоций человека с использованием сверточной нейронной сети на основе аудиоданных

Objectives. This article provides a description and experience of creating the algorithm for recognizing the emotional state of the subject.Methods. Image processing methods are used.Results. The proposed algorithm makes it possible to recognize the emotional states of the subject on the basis of an audio data set. It was possible to improve the accuracy of the algorithm by changing the data set supplied to the input of the neural network.The stages of training convolutional neural network on a pre-prepared set of audio data are described, and the structure of the algorithm is described. To validate the neural network different set of audio data, not participating in the training, was selected. As a result of the study, graphs were constructed demonstrating the accuracy of the proposed method.After receiving the initial data of the study, the analysis of the possibilities for improving the algorithm in terms of ergonomics and accuracy of operation was also carried out. The strategy was developed to achieve a better result and obtain a more accurate algorithm. Based on the conclusions presented in the article, the rationale for choosing the representation of the data set and the software package necessary for the implementation of the software part of the algorithm is given.Conclusion. The proposed algorithm has a high accuracy of operation and does not require large computational costs.Цели. Приведено описание и рассмотрен опыт создания алгоритма распознавания эмоционального состояния субъекта.Методы. Использованы методы обработки изображений.Результаты. Предложенный алгоритм позволяет распознавать эмоциональные состояния субъекта на основании звукового набора данных. Благодаря проведенному исследованию удалось улучшить точность работы алгоритма путем изменения подаваемого на вход нейронной сети набора данных.Описаны этапы обучения сверточной нейронной сети на заранее заготовленном наборе звуковых данных, а также структура алгоритма. Для валидации нейронной сети был отобран иной, не участвующийв тренировке, набор аудиоданных. В результате проведения исследования построены графики, демонстрирующие точность работы предлагаемого метода.После получения первоначальных данных сделан анализ возможностей улучшения алгоритма с точки зрения эргономики и точности его работы. Разработана стратегия, позволяющая добиться лучшего результата и получить более точный алгоритм. На основании заключений, изложенных в статье, приводится обоснование выбора представления набора данных и программного комплекса, необходимого для реализации программной части алгоритма.Заключение. Предложенный алгоритм обладает высокой точностью и не требует больших вычислительных затрат

Affect Recognition in Human Emotional Speech using Probabilistic Support Vector Machines

The problem of inferring human emotional state automatically from speech has become one of the central problems in Man Machine Interaction (MMI). Though Support Vector Machines (SVMs) were used in several worksfor emotion recognition from speech, the potential of using probabilistic SVMs for this task is not explored. The emphasis of the current work is on how to use probabilistic SVMs for the efficient recognition of emotions from speech. Emotional speech corpuses for two Dravidian languages- Telugu & Tamil- were constructed for assessing the recognition accuracy of Probabilistic SVMs. Recognition accuracy of the proposed model is analyzed using both Telugu and Tamil emotional speech corpuses and compared with three of the existing works. Experimental results indicated that the proposed model is significantly better compared with the existing methods

Emotion recognition based on the energy distribution of plosive syllables

We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: \ba, \du, \ki, \ta common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions

Impact of environmental regulation policy on ecological efficiency in four major urban agglomerations in eastern China

Rapid economic development of any country will usually lead to negative environmental impacts. A free market economy cannot fundamentally solve this issue, which requires the guidance and control of the government. The environmental policies of governments can effectively improve the ecological conditions in a region. This study quantifies environmental regulation policies and takes four urban agglomerations in eastern China as the research object to explore the influence of environmental regulation on regional ecological efficiency. First, policies can be divided into policy control, pollution control, ecological protection, and social adjustment by building a Latent Dirichlet Allocation (LDA) model of a policy text library to measure differences in urban policy bias. Next, a slack-based measure (SBM) model was used to measure urban ecological efficiency. Finally, using the qualitative comparison analysis method, the time effect was considered and the ecological efficiency configuration of the entire region was obtained. In addition, contrast analysis was conducted for different path configurations and the reasons of the statuses of the four urban agglomerations were obtained. The results show that policy control, pollution control, ecological protection, and other mandatory policies can significantly reduce the negative environmental effects, especially when the policy controls involve an explicit form of punishment, which is a necessary condition for achieving a high level of ecological efficiency. However, social adjustment means having higher requirements for regional total factor development, which requires improving the environmental awareness of residents and improving corporate social responsibility. Regional differences will mean that areas with a lower level of economic development have a higher intensity of policy control, and the means of policy control should be matched with the level of economic development. In addition, this study proposes some policy suggestions, such as strengthening policy control and pollution prevention, strengthening social regulation, and promulgating environmental laws and regulations according to local conditions