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A real potential exists for library use of voice technologies: as aids to the disabled or illiterate library user, as front‐ends for general library help systems, in online systems for commands or control words, and in many of the hands‐busy‐eyes‐busy activities that are common in libraries. Initially, these applications would be small, limited processes that would not require the more fluent human‐machine communication that we might hope for in the future. Voice technologies will depend on and benefit from new computer systems, advances in artificial intelligence and expert systems to facilitate their use and enable them to better circumvent present input and output problems. These voice systems will gradually assume more importance, improving access to information and complementing existing systems, but they will not likely revolutionize or dominate human‐machine communications or library services in the near future.

The goal of this study is to test the real-world use of an emotion recognition system.

The researchers chose an existing algorithm that displayed high accuracy and speed. Four emotions: happy, sadness, anger and surprise, are used from six of the universal emotions, associated by their own mood markers. The mood-matrix interface is then coded as a web application. Four guidance counselors and 10 students participated in the testing of the mood-matrix. Guidance counselors answered the technology acceptance model (TAM) to assess its usefulness, and the students answered the general comfort questionnaire (GCQ) to assess their comfort levels.

Results from TAM found that the mood-matrix has significant use for the guidance counselors and the GCQ finds that the students were comfortable during testing.

No study yet has tested an emotion recognition system applied to counseling or any mental health or psychological transactions.

Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.

The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.

Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.

A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.

Creating memorable tourism experiences (MTE) is vital to obtain sustained tourism visits. In the digital era, infusions of various digital technologies in tourism services without admitting tourist emotions could jeopardize the experience. Drawing from a Service-Dominant Logic (S-DL) perspective, this study explains the complexity of digital tourism experience in the service system view, highlighting the importance of emotions as resources. It is composed of actors' orchestrations, connected by shared emotions, and enabled by sensory stimuli facilitated by the digital tourism ecosystem throughout the tourism journey. This study proposes a Memorable Digital Tourism Experience (MDTE) framework by identifying the focal actors, recognizing the emotions, and determining the moderating role of sensory stimuli enabled by various novel technologies. At last, several agenda and practical guidelines are proposed on how to operationalize the framework and different methodologies to explore Memorable Digital Tourism Experience.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

Dyadic interactions are significant for human life. Most body sensor networks-based research studies focus on daily actions, but few works have been done to recognize affective actions during interactions. The purpose of this paper is to analyze and recognize affective actions collected from dyadic interactions.

A framework that combines hidden Markov models (HMMs) and k-nearest neighbor (kNN) using Fisher kernel learning is presented in this paper. Furthermore, different features are considered according to the interaction situations (positive situation and negative situation).

Three experiments are conducted in this paper. Experimental results demonstrate that the proposed Fisher kernel learning-based framework outperforms methods using Fisher kernel-based approach, using only HMMs and kNN.

The research may help to facilitate nonverbal communication. Moreover, it is important to equip social robots and animated agents with affective communication abilities.

The presented framework may gain strengths from both generative and discriminative models. Further, different features are considered based on the interaction situations.

This chapter proposes a conceptualization of joyful leadership as a unique leadership style and identifies a future research agenda to further explore the concept. While the concept of joyful leadership appears repeatedly in the nonacademic literature, including in blogs, vlogs, and podcasts, there is limited reference to joyful leadership in the academic literature highlighting a lack of academic rigor around the concept. Joyful leadership is proposed as a unique leadership style with specific patterns of behavior demonstrated by the leader. This research draws on understandings of emotion, positive affect, and leadership in the academic literature to develop a conceptualization of joyful leadership.

The proposed conceptualization is based on an extensive literature review drawing from both the leadership field and the study of emotions including various theoretical perspectives from these diverse fields.

Based on discrete emotion theory a conceptualization of joyful leadership as a unique leadership style is presented, identifying key patterns of behavior associated with joyful leadership including discrete autonomic patterns, actions, nonverbal signals, and identified feelings.

This research outlines a conceptual model to provide an understanding of the concept of joyful leadership as a unique leadership style. It draws on the current study of emotion, positive affect, and leadership and more specifically examines the concept of joyful leadership aligned to discrete emotion theory. This particular theory of emotion, when examined in relation to leadership, provides a basis for the concept of joyful leadership as a leadership style and the basis for its proposed characteristics and outcomes.

Authenticating an individual through voice can prove convenient as nothing needs to be stored and cannot easily be stolen. However, if an individual is authenticating under duress, the coerced attempt must be acknowledged and appropriate warnings issued. Furthermore, as duress may entail multiple combinations of emotions, the current f-score evaluation does not accommodate that multiple selected samples possess similar levels of importance. Thus, this study aims to demonstrate an approach to identifying duress within a voice-based authentication system.

Measuring the value that a classifier presents is often done using an f-score. However, the f-score does not effectively portray the proposed value when multiple classes could be grouped as one. The f-score also does not provide any information when numerous classes are often incorrectly identified as the other. Therefore, the proposed approach uses the confusion matrix, aggregates the select classes into another matrix and calculates a more precise representation of the selected classifier’s value. The utility of the proposed approach is demonstrated through multiple tests and is conducted as follows. The initial tests’ value is presented by an f-score, which does not value the individual emotions. The lack of value is then remedied with further tests, which include a confusion matrix. Final tests are then conducted that aggregate selected emotions within the confusion matrix to present a more precise utility value.

Two tests within the set of experiments achieved an f-score difference of 1%, indicating, Mel frequency cepstral coefficient, emotion detection, confusion matrix, multi-layer perceptron, Ryerson audio-visual database of emotional speech and song (RAVDESS), voice authentication that the two tests provided similar value. The confusion matrix used to calculate the f-score indicated that some emotions are often confused, which could all be considered closely related. Although the f-score can represent an accuracy value, these tests’ value is not accurately portrayed when not considering often confused emotions. Deciding which approach to take based on the f-score did not prove beneficial as it did not address the confused emotions. When aggregating the confusion matrix of these two tests based on selected emotions, the newly calculated utility value demonstrated a difference of 4%, indicating that the two tests may not provide a similar value as previously indicated.

This approach’s performance is dependent on the data presented to it. If the classifier is presented with incomplete or degraded data, the results obtained from the classifier will reflect that. Additionally, the grouping of emotions is not based on psychological evidence, and this was purely done to demonstrate the implementation of an aggregated confusion matrix.

The f-score offers a value that represents the classifiers’ ability to classify a class correctly. This paper demonstrates that aggregating a confusion matrix could provide more value than a single f-score in the context of classifying an emotion that could consist of a combination of emotions. This approach can similarly be applied to different combinations of classifiers for the desired effect of extracting a more accurate performance value that a selected classifier presents.

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

Deep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior.

DL technology is used to design a speech evaluation system.

The experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency.

The results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.