Voice Activity Research Articles

Effect of Partial Deafness on Voice in Children

Elevated hearing thresholds cause disturbances in auditory control of voice. This leads to voice disorders that are described by changes in acoustic parameters of voice measured objectively. Voice changes can also be detected by perceptive assessment with GRBAS scale.This article presents the results of the study performed to analyze voice characteristics in a group of children with prelingual partial deafness (PD), a condition with normal hearing at low frequencies and deep hypoacusis (near deafness) at high frequencies. Voice disorders accompanying partial deafness were not a subject of the intensive study before, therefore our study provides valuable data and help to understand more complex clinical aspects of PD.The study showed that voice characteristics in children with PD differs from children with normal hearing and shows some differences compared to voice of children with other types of hearing impairment. Major changes in PD are seen in frequency (F0, vF0), amplitude (vAm, sAPQ), noise (NHR), and tremor (FTRI) parameters. The changes were present in all groups of objective measures, which makes a difference versus normal hearing; however the most significant changes were seen in a narrower range of parameters compared to children in other types of hearing impairment.Objective parameters correlate with the subjective features of voice of children with partial deafness. With use of GRBAS scale, subjective voice assessment showed elevated indices of grade, roughness, breathy, asthenic and strained voice, which described slightly harsh, breathy, dull, and a little tensed voice.

Voice Controlled Catheterisation Laboratory

Voice-controlled catheterization laboratory is future technology that would be designed to make the process of catheterization safer and more efficient. Cardiac Catheterization is a common procedure including Angiography and Angioplasty. The current method of catheterization uses manual lab, which can be cumbersome, stressful and prone to errors, especially in critical procedures. Voice-controlled catheterization laboratory utilizes voice commands and natural language processing to operate the Cath Lab. The system is programmed to respond to voice commands and execute the desired movement with accuracy and precision. This technology eliminates the need for manual control, reducing the risk of human error and enabling Cardiologists to concentrate on the procedure. In addition, voice control allows for faster, more efficient catheterization, which can save time and resources in a hospital setting. Voice-controlled catheterization laboratory is the future of cardiac procedures, providing a safer and more efficient way to perform catheterization. This article review the technology that is expected to become widely adopted in the near future and will significantly improve the quality of cardiovascular procedure.

Voice Controlled Car

A system is being proposed, which focuses on the concept of how a car can be controlled by the human voice. Voice control car is just a practical example of controlling motions of a simple car by giving voice commands. In this system, an android app is used for the transmission of human commands to Arduino. An Arduino can be interfaced with the Bluetooth module through the UART protocol. The speech is received by the android app and processed by the voice module. Voice is then converted to text. The Arduino will further process this text, which will take suitable action to regulate the robot. The objective is to design a robotic car whose basic movements such as moving forward, turning to left or right can be controlled by the human voice

Enhanced Sign Language Communication through Deep Learning-based Gesture and Action Recognition and Correction

Sign language is a crucial form of communication for the hearing impaired, presenting unique challenges for technological interpretation. This project focuses on developing a Sign Language Detection Using Action Recognition (SLDAR) system to address these challenges. Leveraging Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and multimodal fusion techniques, our system aims to accurately detect and recognize sign language gestures in real-time. The project involves collecting and annotating a diverse dataset comprising video, audio, and text data. We then design and train a deep learning model that combines features from these modalities to improve accuracy and robustness.The project involves collecting and annotating a diverse dataset, designing a deep learning model for multimodal fusion, and implementing a user-friendly interface for real-time interaction. The system's performance is evaluated using metrics such as accuracy, precision, and recall. Our goal is to enhance accessibility and communication for the hearing impaired through the development of this advanced sign language recognition system. Keywords:Acoustic Features, Linguistic Context, Mean Opinion Score (MOS), Natural Language Processing, NLP Algorithms, Signal-to-Noise Ratio (SNR), Speech Enhancement, Speech Quality, Telecommunication Systems, Voice Assistants.

Ascribing consciousness to artificial intelligence: human-AI interaction and its carry-over effects on human-human interaction.

The question of whether artificial intelligence (AI) can be considered conscious and therefore should be evaluated through a moral lens has surfaced in recent years. In this paper, we argue that whether AI is conscious is less of a concern than the fact that AI can be considered conscious by users during human-AI interaction, because this ascription of consciousness can lead to carry-over effects on human-human interaction. When AI is viewed as conscious like a human, then how people treat AI appears to carry over into how they treat other people due to activating schemas that are congruent to those activated during interactions with humans. In light of this potential, we might consider regulating how we treat AI, or how we build AI to evoke certain kinds of treatment from users, but not because AI is inherently sentient. This argument focuses on humanlike, social actor AI such as chatbots, digital voice assistants, and social robots. In the first part of the paper, we provide evidence for carry-over effects between perceptions of AI consciousness and behavior toward humans through literature on human-computer interaction, human-AI interaction, and the psychology of artificial agents. In the second part of the paper, we detail how the mechanism of schema activation can allow us to test consciousness perception as a driver of carry-over effects between human-AI interaction and human-human interaction. In essence, perceiving AI as conscious like a human, thereby activating congruent mind schemas during interaction, is a driver for behaviors and perceptions of AI that can carry over into how we treat humans. Therefore, the fact that people can ascribe humanlike consciousness to AI is worth considering, and moral protection for AI is also worth considering, regardless of AI's inherent conscious or moral status.

Coimagining the Future of Voice Assistants with Cultural Sensitivity

Voice assistants (VAs) are becoming a feature of our everyday life. Yet, the user experience (UX) is often limited, leading to underuse, disengagement, and abandonment. Co-designing interactions for VAs with potential end-users can be useful. Crowdsourcing this process online and anonymously may add value. However, most work has been done in the English-speaking West on dialogue data sets. We must be sensitive to cultural differences in language, social interactions, and attitudes towards technology. Our aims were to explore the value of co-designing VAs in the non-Western context of Japan and demonstrate the necessity of cultural sensitivity. We conducted an online elicitation study (N=135) where Americans (n=64) and Japanese people (n=71) imagined dialogues (N=282) and activities (N=73) with future VAs. We discuss the implications for coimagining interactions with future VAs, offer design guidelines for the Japanese and English-speaking US contexts, and suggest opportunities for cultural plurality in VA design and scholarship.

AI HealthCare Chat Bot System

Through chatbots one can communicate with text or voice interface and get reply through artificial intelligence. Typically, a chat bot will communicate with a real person Chatbots are programs built to automatically engage with received messages. Chatbots can be programmed to respond the same way each time, to respond differently to messages containing certain keywords and even to use machine learning to adapt their responses to fit the situation. This healthcare chatbot system will help hospitals to provide healthcare support online 24 x 7, it answers deep as well as general questions. It also helps to generate leads and automatically delivers the information of leads to sales. Key Words: Chat Bot , healthcare, artificial, intelligence.

Voice Control Armed Robot Assistant Using Arduino Uno By IJISRT

In this paper we implement intelligent system that follow voice commands and controlling using Arduino UNO. The proposed system will perform several tasks through voice commands. Also, it will interact with user by answering the questions asked by user. In proposed system Arduino uno is used to read voice commands and response them. The aim of this system is to assist user physically and also virtually. The system is helpful for students, Tech enthusiasts, and physically disabled people to do their daily work and help in problems. This Voice Control Assistant will be helpful for elderly and physically disabled also. The armed Voice Assistant can pick up and place things over voice commands. This feature enhances capabilities of assistant.

Proposing Usability-UX technologies for the design and evaluation of text-based chatbots

Chatbots are interactive systems that communicate using natural language with human users, via a textual interface or voice activation. These tools are useful for many spheres of business such as Customer Service, Sales, Education and Learning, Health and Entertainment. Recently, chatbots have become popular, with significant growth in the software industry, especially text-based chatbots. This is encouraging developers to create their own tools, as well as attracting efforts from researchers into this area. Despite this highlight, technologies to guarantee the quality of chatbots and user satisfaction are not keeping up with the growing demand for these tools. Considering this, there is a need to propose technologies capable of supporting developers and development teams in the process of building and evaluating chatbots. Therefore, this research proposes to develop artifacts applicable to the design and evaluation process of chatbots, based on quality attributes identified in systematic literature reviews related to Usability and User Experience (UX), due to the importance and impact that these aspects have on user satisfaction and the perceived quality of the system. The first artifact is the U2Chatbot inspection checklist, developed to assist development teams in the process of identifying defects in text-based chatbots. The second artifact is a set of interface design patterns, DP-U2Chatbot, containing useful examples to support developers in the process of building chatbots. The technologies were subjected to the necessary evaluations. The results of the empirical study regarding the U2Chatbot inspection checklist indicated that participants considered the technology useful for discovering defects in chatbots, however, ease of use could be improved. The participants' experience discreetly influenced the effectiveness and efficiency of the technique, leading us to believe that professionals with a certain level of inspection experience can benefit more from the checklist. Regarding the evaluation of DP-U2Chatbot design patterns, the results generally indicated that the technology is easy to understand and useful in supporting the design of chatbots, helping to build better tools.

The Impact of Artificial Intelligence (AI) on mobile App Development

Artificial Intelligence (AI) has revolutionized the landscape of mobile app development, bringing about transformative changes in functionality, user experience, and capabilities. This paper aims to explore and analyze the impact of AI on mobile app development, highlighting its significant contributions and potential implications. The integration of AI technologies such as machine learning, natural language processing, and computer vision has empowered mobile applications to offer personalized experiences, enhanced decision-making capabilities, and predictive functionalities. AI-driven algorithms enable apps to analyze vast amounts of data, allowing for real-time insights and intelligent responses, thereby optimizing user engagement and satisfaction. Moreover, AI has facilitated the development of innovative features like voice assistants, recommendation systems, and augmented reality experiences, fostering a more intuitive and interactive user interface. These advancements have not only enriched the user experience but also opened new avenues for businesses to better understand their users and tailor services accordingly. However, with these advancements come challenges, including privacy concerns, ethical considerations, and the need for robust security measures to safeguard sensitive user data. As AI-powered mobile apps become more prevalent, it is imperative to address these challenges to ensure responsible and ethical use of AI technologies. This paper examines the evolution of AI in mobile app development, evaluates its impact on user experience and functionality, discusses the challenges and ethical considerations, and provides insights into the future trends and opportunities that AI presents in shaping the landscape of mobile app development. Understanding the profound impact of AI on mobile app development is crucial for developers, businesses, and stakeholders to harness its full potential while navigating the associated challenges responsibly.

When the recipe is more important than the ingredients: Unveiling the complexity of consumer use of voice assistants

AbstractThe widespread use of voice assistants (VAs) creates a pressing need to understand what drives consumers to use different VAs. Existing studies have commonly focused on the net effects of antecedents that explain why consumers adopt or continue using VAs, ignoring the complexity of consumer behavior and the combinatorial effects of multiple antecedents. Our study proposes that consumer intention to continue using VAs does not depend on a single characteristic of products or consumers but on specific configurations of such characteristics. By integrating human–technology interaction and media richness theories, we suggest that consumers with distinct psychometric profiles and learning styles may evaluate humanlike and technological attributes of VAs differently. Our study shows that the complex interconnectedness between different VA attributes and consumer characteristics can provide a holistic understanding of why some consumers continue or stop using VAs. The results advance the media richness literature by offering novel insights into multimodality in consumer–technology interactions by examining consumer evaluations of single and multimodal VAs (e.g., smart speakers vs. touchscreen smart speakers). Our study provides templates for managers to effectively design VAs aligned with their segmentation and targeting strategies.

The improvement of oral communicative competence in english through the artificial intelligence

The study "Improvement of Oral Communicative Competence in English through the Artificial Intelligence" focused on exploring how artificial intelligence technology can influence the development of oral communication skills during the teaching and learning process of English as a second language, with the participants of this research being future teachers of the subject. This research aimed to determine the effectiveness of artificial intelligence applications, such as voice assistants and speech recognition systems, in improving students' oral communicative competence. The study examined how these artificial intelligence systems provided instant and personalized feedback, as well as opportunities for continuous oral practice for students. It also explored how artificial intelligence can adapt to the individual needs of students, which was beneficial for those with different skill levels or specific learning needs. The results of this research demonstrated that teaching and learning mediated by artificial intelligence had significant implications, suggesting how artificial intelligence technology can play an integral role in the development of oral communicative competence in English and possibly other languages. Finally, this teaching approach promises to facilitate more effective and accessible learning for a larger number of students, enhancing their oral communication skills in the process.

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification.

In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

Laser-Based Command Injection Attacks on Voice-Controlled Microphone Arrays

Voice-controlled (VC) systems, such as mobile phones and smart speakers, enable users to operate smart devices through voice commands. Previous works (e.g., LightCommands) show that attackers can trigger VC systems to respond to various audio commands by injecting light signals. However, LightCommands only discusses attacks on devices with a single microphone, while new devices typically use microphone arrays with sensor fusion technology for better capturing sound from different distances. By replicating LightCommands’s experiments on the new devices, we find that simply extending the light scope (just as they do) to overlap multiple microphone apertures is inadequate to wake up the device with sensor fusion. Adapting LightCommands’s approach to microphone arrays is challenging due to their requirement for multiple sound amplifiers, and each amplifier requires an independent power driver with unique settings. The number of additional devices increases with the microphone aperture count, significantly increasing the complexity of implementing and deploying the attack equipment. With a growing number of devices adopting sensor fusion to distinguish the sound location, it is essential to propose new approaches to adapting the light injection attacks to these new devices. To address these problems, we propose a lightweight microphone array laser injection solution called LCMA (Laser Commands for Microphone Array), which can use a single laser controller to manipulate multiple laser points and simultaneously target all the apertures of a microphone array and input light waves at different frequencies. Our key design is to propose a new PWM (Pulse Width Modulation) based control signal algorithm that can be implemented on a single MCU and directly control multiple lasers via different PWM output channels. Moreover, LCMA can be remotely configured via BLE (Bluetooth Low Energy). These features allow our solution to be deployed on a drone to covertly attack the targets hidden inside the building. Using LCMA, we successfully attack 29 devices. The experiment results show that LCMA is robust on the newest devices such as the iPhone 15, and the control panel of the Tesla Model Y.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

Factors influencing older adults' acceptance of voice assistants.

Voice assistants (VAs) have the potential to uphold and enhance the quality of life for older adults. However, the extent to which older adults accept and benefit from VAs may be relatively modest. This study developed a comprehensive model combined with product and personal characteristics to explain the acceptance of VAs among older adults, using semi-structured interviews (Study 1) and questionnaires (Study 2). Results revealed that in terms of product characteristics, perceived usefulness and perceived enjoyment significantly affect behavior intention. Regarding personal characteristics of older adults, technological self-efficacy and dispositional resistance to change significantly affect behavior intention. However, no direct impact of perceived ease of use and perceived trust on behavior intention. Additionally, perceived enjoyment influenced both perceived ease of use and perceived usefulness. Results suggested the significant role of technology self-efficacy and dispositional resistance to change in predicting the acceptance of VAs among older adults. Our newly developed model offers valuable insights for tailoring VAs to this demographic during design and implementation.

Green Transitions in Developing Countries: Perspectives on Women’s Political Leadership

Green transition by reducing CO2 emissions and curtailing the exploitation of natural resources while increasing renewable energy use and generation is one of the most important targets in sustainable development. This study investigated the impacts of women’s empowerment in politics on the green transition in developing countries. We applied a fixed-effects model to the panel data for 131 developing countries from 2000 to 2019. Our findings indicated that higher levels of participation by women in politics are likely to reduce CO2 emissions and reliance on natural resources. However, there was no statistically significant effect of women’s political empowerment on renewable energy consumption. Women’s political participation enhanced the transition towards sustainable energy sources such as geothermal and wind energy. We also found that the effects of women’s political participation on CO2 emissions were greater in countries with better political stability, government effectiveness, regulatory quality, rule of law, control of corruption, and voice and accountability. This study contributes to the growing literature in feminism and environmentalism on the achievement of sustainable development. JEL Codes: J16, O13, P48

Got It? Comparative Ergonomic Evaluation of Robotic Object Handover for Visually Impaired and Sighted Users

The rapidly growing research on the accessibility of digital technologies has focused on blind or visually impaired (BVI) users. However, the field of human–robot interaction has largely neglected the needs of BVI users despite the increasing integration of assistive robots into daily life and their potential benefits for our aging societies. One basic robotic capability is object handover. Robots assisting BVI users should be able to coordinate handovers without eye contact. This study gathered insights on the usability of human–robot handovers, including 20 BVI and 20 sighted participants. In a standardized experiment with a mixed design, a handover robot prototype equipped with a voice user interface and haptic feedback was evaluated. The robot handed over everyday objects (i) by placing them on a table and (ii) by allowing for midair grasping. The usability target was met, and all user groups reported a positive user experience. In total, 97.3% of all handovers were successful. The qualitative feedback showed an appreciation for the clear communication of the robot’s actions and the handover reliability. However, the duration of the handover was seen as a critical issue. According to all subjective criteria, the BVI participants showed higher variances compared to the sighted participants. Design recommendations for improving robotic handovers equally supporting both user groups are given.

Good Governance and Tourism in South Asian Emerging Economies: The Mediating Role of Foreign Direct Investment

This study examined the role of good governance in promoting tourism in emerging South Asian countries and also tested the mediating role of foreign direct investments (FDI). The study used panel data collected from seven South Asian countries from 1996 to 2018. Panel data regression models were estimated to test the research hypotheses. The results showed that the country’s good governance has a positive and significant influence on tourism receipts and arrivals. Moreover, the mediation analysis results supported that foreign direct investments mediated the relationship between good governance indicators like government effectiveness, political stability, voice and accountability, and control over corruption and tourism. Based on the results, the study proposed the governance-led FDI and FDI-led tourism hypotheses, the study’s main contribution. The study has implications for the government regulatory agencies relating to governance, tourism departments, foreign prospect investors, and policymakers.

Design and Development of Automated Control of Restaurant Robot

This paper presents a novel voice-controlled restaurant robot system designed to revolutionize the dining experience by seamlessly integrating advanced robotics and natural language processing technologies. The proposed system aims to enhance customer service, operational efficiency, and overall dining satisfaction by providing a user-friendly and interactive platform. .The restaurant robot is equipped with a comprehensive set of capabilities, including, voice recognition and manipulation. By leveraging voice control, users can interact with the robot using natural language commands, enabling seamless communication and intuitive control. This allows customers to effortlessly place orders The system's is comprises a mobile robotic platform, bluetooth modules, a cloud-based natural language processing, The voice control functionality is implemented through a sophisticated speech recognition system, enabling the robot to accurately understand and interpret user commands. .In addition to customer-facing interactions, the restaurant robot also assists restaurant staff in various tasks. Deliver food and beverages to tables, handle payment transactions, and perform routine cleaning tasks. By automating these processes, the system improves operational efficiency, reduces labor costs, and allows human staff to focus on highervalue activities such as customer engagement and culinary expertise. .To evaluate the performance and usability of the voice-controlled restaurant robot, a series of experiments were conducted in a real-world restaurant environment. The results demonstrated the system's high recognition accuracy, robustness in noisy environments, and its ability to effectively handle a wide range of user requests. .The proposed voice-controlled restaurant robot presents a promising solution for transforming the dining experience. It combines the convenience of voice control with advanced robotics, enabling seamless and interactive interactions between customers, staff, and the robotic platform. The system's ability to enhance customer service, operational efficiency, and overall dining satisfaction positions it as a significant advancement in the hospitality industry.