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Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.

Increased demands on catalogers to increase productivity, while maintaining quality and accuracy, can be difficult to meet, and may even be hazardous to their health. OCLC Passport for Windows (PFW) and CatME provide some ability to create macros and shortcuts to speed cataloging activities, but do not solve the problems created by long work sessions. Voice recognition software, which works with most applications, is an effective way to input commands and data accurately into a computer. The software can be customized to provide single word input of complex commands, allowing the cataloger to concentrate more on the content of their records, rather than the input. This type of software is helpful not only to workers with repetitive strain injury (RSI) or physical disabilities, but also to anyone requiring accurate input during extended work sessions. This article explains how the software works, offers examples, and provides tips on working with using voice recognition.

The purpose of this paper is to design an interactive industrial robotic system which can be used to assist a “layperson” in re‐casting a generic pick‐and‐place application. A user can program a pick‐and‐place application simply by pointing to objects in the work area and speaking simple and intuitive natural language commands.

The system was implemented in C# using the EMGU wrapper classes for OpenCV as well as the MS Speech Recognition API. The target language to be recognized was modelled using traditional augmented transition networks which were implemented as XML Grammars. The authors developed an original finger‐pointing algorithm using a unique combination of standard morphological and image processing techniques. Recognized voice commands trigger the vision component to capture what a user is pointing at. If the specified action requires robot movement, the required information is sent to the robot control component of the system, which then transmits the commands to the robot controller for execution.

The voice portion of the system was tested on the factory floor in a “typical” manufacturing environment, which was right at the maximum allowable average decibel level specified by OSHA. The findings show that a modern/standard MS Speech API voice recognition system can achieve a 100 per cent accuracy of simple commands; although at the noisy levels of 89 decibels on average, every one out of six commands had to be repeated. The vision component was test of 72 test subjects who had no prior knowledge of this work. The system accurately recognized what the test subjects were pointing at 95 per cent of the time within five seconds of hand readjusting.

The vision component suffers from the “typical” problems: very shiny surfaces can cause problems; very poor contrast between the pointing hand and the background; and occlusions. Currently the system can only handle a limited amount of depth recovery using a spring mounted gripper. A second camera (future work) needs to be incorporated in order to handle large depth variations in the work area.

This system could have a huge impact on how factory floor workers interact with robotic equipment.

The testing of the voice system on a factory floor, although simple, is very important. It proves the viability of this component of the system and debunks arguments that factories are simply too noisy for current voice technology. The unique finger‐pointing algorithm developed by the authors is also an important contribution to the field. In particular, the manner in which the pointing vector was constructed. Furthermore, very few papers report results of non‐experts using their pointing algorithms. The paper reports concrete results that show the system is intuitive and user friendly to “laypersons”.

Launched in 2014, Amazon's Echo and Echo Dot smart speakers led the category's rapid adoption by households and enabled the penetration of artificial intelligence (AI) voice assistants into the everyday lives of millions of people. By 2019, Alexa the virtual brains behind Amazon's smart speakers was able to play music, create reminders, get weather reports, control lights and other home appliances, shop, and do much more in response to voice commands. Amazon had developed significant new capabilities for Alexa, developed an entire ecosysgtem around it, expanded Alexa's user base to more than 100 million users, and made significant progress in monetizing its digital voice assistant. However, Alexa's progress also created new challenges for Amazon, its Alexa-enabled customers, and society at large. Amazon needed to identify and address these challenges in order to encourage continued consumer acceptance and preclude detrimental government or regulatory action.

The purpose of this paper is to identify and analyse the difficulties encountered in the implementation of a voice picking system at a large multinational company of the tractor industrial segment, outlining a comparison related to the main critical factors concerning the system implementation at a Brazilian and a USA plant.

The methodology utilised was qualitative and exploratory conducted through case studies in the two plants of the multinational company. The main data were collected through interviews with key managers directly involved in the project of voice picking system implementation.

The results indicate that the picking processes in the two plants were similar, since both were designed for the production line and conducted using bar-code readers and paper lists. Nevertheless, the internal warehousing process in the USA was more mature and computerised, whereas the Brazilian process still had opportunity for improvement, such as, the visual storage process, where the operator was responsible for locating an empty position.

Since this research is an exploratory case study, its results cannot be generalised.

The paper provides relevant practical information and experiences to managers interested in implementing voice picking systems, as well as interested in improving the accuracy and productivity of logistics processes within warehouses.

The voice picking systems are more widespread in the USA than in Brazil, and therefore, companies around the world can use this studied case to better understand about the voice picking systems implementation process in both emerging and mature marketplaces.

The purpose of this study is to discuss the development of Android-based Intelligent Software Assistant application for visually challenged or blind people. The application is intended to help people with visual limitations or blind people to access Android-based devices so that they can use library resources by using android devices.

The necessary data are collected from journals, articles, books and questionnaires, and similar applications are analyzed. The application design method used is the Scrum method, which consists of Backlog, Sprint and Scrum Meeting. From the operational side of the application, the method used is speech-to-text and text-to-speech.

This application has been tried with some users who have total blindness and low vision, and all provided a good response to this application. From the performance side, the user gives a very satisfied response to this application. While the ease of using the application, the user also provides a satisfactory response to the ease of using this application.

The application still has limitations in penetration to the user, and the application is only built using Android as its platform. In addition, the dependence on libraries from Google has caused difficulties in implementing this application with local dialect, which is only understood by the local community.

This application has implications for the society, especially those with limitations in eyesight can be so much more productive and independent. This can reduce the social burden in society.

This application provides an easy access of an android device to blind people and people with low vision, as well as access to library resources with devices that have been installed with this application. This facility can improve the library accessibility to the blind and visually challenged community.

The main objective of this chapter is broadening the understanding of anthropomorphic artificial intelligence (AI) (e.g. avatars, humanoid robots, chatbots) in both physical and digital environments. The chapter strives to demonstrate how organisations can curate relationship marketing and enhance customer experience by employing anthropomorphic AI. To achieve this, the chapter extends existing understanding in three ways. First, it explains the interconnectivity between relationship marketing and customer experience. Second, it presents anthropomorphic AI along with its different characteristics and technologies. Third, it offers some real-life uses cases and examples of such AI drawing from practical insights into five selected industries. Overall, the chapter provides some food of thoughts concerning the successful application and deployment of anthropomorphic AI in marketing practices.

The aim of this paper is to propose a grasping method based on intelligent perception for implementing a grasp task with human conduct.

First, the authors leverage Kinect to collect the environment information including both image and voice. The target object is located and segmented by gesture recognition and speech analysis and finally grasped through path teaching. To obtain the posture of the human gesture accurately, the authors use the Kalman filtering (KF) algorithm to calibrate the posture use the Gaussian mixture model (GMM) for human motion modeling, and then use Gaussian mixed regression (GMR) to predict human motion posture.

In the point-cloud information, many of which are useless, the authors combined human’s gesture to remove irrelevant objects in the environment as much as possible, which can help to reduce the computation while dividing and recognizing objects; at the same time to reduce the computation, the authors used the sampling algorithm based on the voxel grid.

The authors used the down-sampling algorithm, kd-tree algorithm and viewpoint feature histogram algorithm to remove the impact of unrelated objects and to get a better grasp of the state.

Recent advancements in wearable computing offer opportunities for art galleries to provide a unique experience. However, to ensure successful implementation of this new technology in the visitor industry, it is essential to understand user requirements from a visitor’s point of view. Therefore, the aim of this paper is to investigate visitors’ requirements for the development of a wearable smart glasses augmented reality (AR) application in the museum and art gallery context.

Interviews with 28 art gallery visitors were conducted and an affinity diagram technique was used to analyze the interviews.

The findings reveal that wearable AR is in its infancy and that technical and design issues have to be overcome for a full adoption. It reveals that content requirement, functional requirement, comfort, experience and resistance are important when developing and implementing the wearable AR application in the museum and art gallery contexts.

Mapping user requirements in the wearable smart glasses AR context using an affinity diagram is a new approach and therefore contributes to the creation of knowledge in the tourism domain. Practically, the area of wearable technologies and AR within the tourism and visitor industry context is still relatively unexplored, and the present paper provides a first foundation for the implementation of wearable smart glasses AR applications in the museum and art gallery context.