Voice Assistant System Research Articles

Face Recognition-Based Authenticated Voice Assistant System

Face Recognition-Based Authenticated Voice Assistant System

Voice Assistant for Blind Person

This paper presents a comprehensive study on the design, implementation, and evaluation of a voice assistant system tailored specifically for visually impaired individuals. With the proliferation of voice-enabled devices, there exists a promising opportunity to empower visually impaired users through natural language interaction. Our proposed system incorporates advanced speech recognition, natural language understanding, and synthesis techniques to provide intuitive and efficient communication channels. Additionally, we address the unique challenges faced by visually impaired users, such as contextual understanding, accessibility, and usability. Through user studies and evaluations, we demonstrate the efficacy and usability of the proposed system, highlighting its potential to significantly enhance the independence and quality of life for visually impaired individuals in various daily tasks and activities

Enhancing Voice Assistants with Generative AI Language Models for Interactive Conversations

This paper presents comprehensive research for the development of a voice assistant application using HTML/CSS, JavaScript, Python, SQLite, and the Hugging Face API. The proposed methodology encompasses key steps in data collection, model training, frontend and backend development, integration, testing, and deployment, ensuring the creation of a robust and user-friendly voice assistant system. By leveraging a combination of web technologies and Al tools, the developed voice assistant application offers an interactive and intelligent user experience across its users. Key Words: Voice assistant, HTML/CSS, JavaScript, Python, SQLite, Hugging Face API, LLMs, AI.

Enhancing Healthcare Data Entry Efficiency and Accuracy through Voice Assistant Systems: A Case Study of Obijackson Hospital Okija

Traditional data entry methods are often time-consuming and prone to errors, negatively impact patient care and administrative workflows. To address these challenges, this research proposed a voice-assisted data entry system that leverages advanced speech recognition and natural language processing technologies. Object Oriented Analysis and Design Methods were used to implement a system that allows healthcare providers to input patient data and medical information through voice commands, streamlining the data entry process and reducing the cognitive load on healthcare professionals. The system’s design incorporates user-centred principles, ensuring it is intuitive and seamlessly integrated into existing healthcare IT infrastructures. Key features include real-time speech-to-text conversion, contextual understanding of medical terminology, and robust data security measures to protect patient information. The implementation phase involved rigorous testing in a simulated healthcare environment, assessing the system’s accuracy, speed, and user satisfaction. Results indicated significant improvements in data entry efficiency and reduced error rates compared to manual entry methods. Feedback from healthcare professionals highlighted the system’s potential to enhance productivity and patient care quality. This dissertation contributes to the field of healthcare informatics by providing a practical solution to a critical problem, demonstrating the feasibility and benefits of voice-assisted technologies in medical data management. Future work will explore further refinements and the potential for broader adoption across diverse healthcare settings.

AI Based Computer Assistant using Python

Abstract: Our proposed system AVATAR (AI Virtual Assistant Technology for Automatic Response) is an innovative voice assistant system, combining Artificial Intelligence (AI) and Python for human-like interactions. It seamlessly executes a range of functions, from dispatching emails to conducting searches on Wikipedia. The system's design incorporates essential Python packages and ultrasonic sensors for object detection and face recognition. Python's extensive libraries and clean syntax make it the optimal language for this project. AVATAR's security measures include biometric authentication and password protection. While internet connectivity is crucial for optimal performance, the system reliably operates within its designated input range. This research represents a significant leap in AI-driven applications, enhancing efficiency and user experience. Additionally, individuals who are blind or have amputations can utilize AVATAR, as the system is entirely voice-controlled.

Economic assessment of the cost of developing a voice assistant for oncology patients

Subject. Voice assistant is a developing digital healthcare tool that arouse great interest due to its ease of use. In the literature, there is insufficient data on its use in oncology, the cost of development, and experience in domestic platforms application. Objectives. The study aims to develop an approach to calculating the cost of creating a skill in the Alice voice assistant system for patients with skin cancer. Methods. The experience of creating a skill for patients with breast cancer is taken as a basis. We used the methodology for calculating the cost of software product under the calculation method by item. To estimate actual programming costs, we conducted a timekeeping experiment. The model contained 50 scripts with 50,000 lines of code. To evaluate the productivity of IT developers, we applied the volume of software texts, and the number of lines was taken as the number of operators. Results. The total cost of developing a skill for "Alice" is 2,097,190.76 RUB, including the salary fund of the core staff (61%), overhead costs (33.2%), electricity cost (0.2%), depreciation (0.2%). In the total share of labor cost of the core staff, programming costs prevail (61%). Conclusions. The approach to cost calculation for creating a skill for "Alice" is proposed for the first time. We simulated and predicted costs of developing a voice assistant for patients with skin cancer. The development of the skill scenario and the format of the dialog tree are critical and require creating a working group of specialists who contact patients at different stages. The development of the "Alice" skill using the Python can be classified as medium type of complexity, as model solutions and standard algorithms are used in the process.

An expandable voice user interface as lab assistant based on an improved version of Google’s speech recognition

Voice assistants are potentially helpful when working in a scientific laboratory. A big challenge is the extremely specific use of language in every laboratory. As with any voice assistant, another concern is data security. Here, we present Rainbow—an open source voice user interface (VUI) for scientific laboratories, that is adaptable to any Windows PC with Internet access. We used Google Translate Site (GTS) as a voice input and output system to ensure communication to the user. The scripting language AutoIt controls GTS, executes all actions and builds the VUI. Rainbow performs tasks from three different areas—general Microsoft Windows tasks, lab-specific tasks, and device-specific tasks. We achieved significantly higher speech recognition accuracy with our VUI than with GTS alone (91.3% versus 85.1%). Because of Rainbow's architecture, it is possible to improve the voice assistant in terms of functionality and accuracy, allowing each laboratory to optimize its own Rainbow system in a user-friendly way. In a test setup, this led to a speech recognition accuracy of 98.6%. Taken together, Rainbow provides an opportunity for every scientist to implement highly specific scientific terms and tasks to this open source voice assistant system in a very user-friendly way.

Investigating User Preferences for Conversation Design of Voice Assistant Systems using Linguistic Features

As the use of voice assistant (VA) systems is increasing, conversation design in the system is important for effective human-system interaction. The objective of the study was to investigate the level of user preference for VA outputs in terms of linguistics. Answers of three VA systems for each of the nine questions were collected and categorized for distinctive linguistic factors such as type of theme, thematic progression, number of predications, and ellipsis. The VA answers were evaluated through an online survey. Results show that linguistic factors and features significantly affect user preference for VA outputs. The results imply that the linguistic features need to be considered for designing voice interaction communications as a natural interaction method.

SMART ASSISTANT FOR BLIND AND VISUALLY CHALLENGED WITH OBJECT, VOICE, TEXT AND COLOR RECOGNITION

Independence is crucial for visually impaired people to lead fulfilling lives and participate fully in society. It enables them to navigate their surroundings, make independent decisions, and engage in various activities without constant reliance on others. Independence fosters self-confidence, self-esteem, and a sense of control over one's life. It also promotes equal opportunities and inclusion, allowing visually impaired individuals to pursue education, employment, and social interactions on their terms. Among these, the focus on creating assistive technologies for the blind and visually challenged community has gained significant attention. This paper presents smart assistant system for blind and visually challenged to live their life to the fullest. This proposed model ensures to work with an ability to recognize objects, text recognition and color recognition. Additionally we included inbuilt voice assistant system that is AI powered technologies that can respond and understand the spoken language commands. The Smart Assistant for blind and visually challenged individuals presents a comprehensive solution to empower this community with increased independence and enhanced access to information. Keywords: visually impaired, object recognition, color recognition, voice assistant.

Towards Intelligent Aviation: The Integration of Voice Assistants in Autonomous Aircraft

This paper explores the integration of a virtual assistant into an autonomous aircraft, focusing on the utilization of cutting-edge technologies for automatic speech recognition, speech synthesis, and natural language processing. By leveraging the power of Edge AI, particularly NVIDIA Jetson devices, we demonstrate how this amalgamation of technologies creates a lightweight, mobile, and efficient voice assistant system. We introduce a novel approach to enhance the operational capabilities of autonomous aircraft, facilitating human-machine interaction and enhancing overall user experience. This research paves the way for the application of edge-based voice assistants in autonomous vehicles, offering a glimpse into the future of intelligent and responsive in-flight systems.

PATIENT PERCEPTIONS OF USING VOICE-BASED DIETARY ASSESSMENT TOOLS AMONG OLDER ADULTS

Abstract Poor diet among older adults is a risk factor for developing multiple chronic diseases. Dietary recall comprises an important component in intervention research and clinical care. Commonly used tools include the web-based automated self-administered 24-hour assessment (ASA-24). Yet voice assistant (VAS) systems (i.e., Amazon Alexa) have not been developed for this purpose. Hence, we evaluated patient perceptions on performing a VAS-based dietary assessment among older adults. Community-dwelling adults (age 65+ years) participated in two virtual sessions who reported their past 24-hour intake, first using ASA-24, and then using a VAS. All completed a Likert questionnaire (binary, % strongly agree/strongly disagree reported) regarding the simplicity of using both systems, completion time, and user satisfaction. Semi-structured interviews allowed us to ask about technology use. Of the 40 participants (100% enrolled), mean age was 69±1.0 years (85% female, 100% white, 5% Latinx). Only 40% owned a VAS; 60% reported having VAS experience prior to the study. After completing both sessions, 80% preferred a VAS over the ASA-24. Participants reported that web-based recalls were unnecessarily complex (60%), time-consuming (50%), and 60% did not wish to use them. Comparatively, VAS recalls were intuitive (75%), easily reportable (85%), and there was willingness to report food while preparing meals (85%). In 16 participants, we evaluated themes of VAS use including easier navigation, less time, and ability to have a natural conversation. A VAS provides a more convenient, conversational, and computerless interaction to report meals over web-based solutions suggesting they hold promise for dietary recall in older adults.

Semantic-Fuzzing-Based Empirical Analysis of Voice Assistant Systems of Asian Symbol Languages

Recently, smart voice assistants (VAs) are widely deployed to provide control services via voice commands in IoT systems, e.g., smart home, industrial IoT systems, etc. However, due to the complexity of the application environment and the diversity of voice commands, more and more attacks against VAs cause severe security problems. As voice development platforms allow third-party voice skills to be accessed, adversaries are able to obtain users’ private information by squatting attacks using confusing names. The existing work studied the exploitability of semantic misinterpretation in VA systems on phonetic languages such as English. However, due to the semantic structural difference between phonetic English and symbol-based Asian languages, such as Chinese, the linguistic-model-guided fuzzing tool proposed by the previous work is insufficient to conduct semantic analysis on the VAs of Asian Languages. In this article, we conduct a systematic analysis to evaluate the feasibility of voice misinterpretation attacks to typical Asian language VAs through semantic fuzzing. We develop Harmony-Fuzzer, the semantic fuzzing tool that the fuzzing process is under the guidance of fuzzing rules abstracted from phenomena of speech errors, disfluency, or semantically similar expressions in Chinese corpus. We use Bayesian networks to formulate fuzzing models statistically so that the fuzzing space can be controlled by the probability of fuzzing processing. We use our results to test VAs and design malicious skills to empirically verify the feasibility of squatting attacks. We found that squatting attacks on Chinese VAs are feasible when attackers leverage some linguistic phenomena delicately.

Advance Digital Voice Assistance with Voice Recognition Intelligence

In this modern era, day to day life become smarter & interlinked with technology. We already know some voice assistant like google, Siri etc. Now in our voice assistant system, it can act as your smart friend, daily schedule manager, to do writer, calculator & search tool. This project works on speech input & give output through speech &text on screen. This assistant attaches with the world wide web to provide result that the user required. Natural language processing algorithm helps machines to engage in communication using natural human language in many forms.

Recognition of Human Emotions by Voice in the Fight against Telephone Fraud

Advances in communication technologies have made communication between people more accessible. In the era of information technology, information exchange has become very simple and fast. However, personal and confidential information may be available on the Internet. For example, voice phishing is actively used by intruders. The harm from phishing is a serious problem all over the world, and its frequency is growing. Communication systems are vulnerable and can be easily hacked by attackers using social engineering attacks. These attacks are aimed at tricking people or businesses into performing actions that benefit attackers, or providing them with confidential data. This article explores the usefulness of applying various approaches to training to solve the problem of fraud detection in telecommunications. A person's voice contains various parameters that convey information such as emotions, gender, attitude, health and personality. Speaker recognition technologies have wide areas of application, in particular countering telephone fraud. Emotion recognition is becoming an increasingly relevant technology as well with the development of voice assistant systems. One of the goals of the study is to determine the user model that best identifies fraud cases. Machine learning provides effective technologies for fraud detection and is successfully used to detect such actions as phishing, cyberbullying, and telecommunications fraud.

My Voice Assistant Using Python

Abstract: In the current situation of technology development within the field of augmented reading and voice assistant systems development, many researchers are writing in this regard for the development of new and effective technologies. The voice assistant are software package agents that may interpret human speech & responds via synthesized voices apple siri, microsoft cortana & google assistant are the foremost common voice assistant & embedded in smartphones .Today the technological advancement is increasing day by day. with the assistance of computer system in which we are able to do solely few task however currently artificial intelligence ,machine learning and few a lot of technologies have created computer system so advance that we are able to implement any type of task. In the present paper, The thought of speech technology extremely encompasses 2 technologies: Synthesizer and recognizer. A speech synthesizer takes as input and produces an audio stream as output. A speech recognizer on the opposite hand does opposite. It takes an audio stream as input and thus turns it into text transcription. The voice may be a signal of infinite information. for this reason, we have got created our own virtual personal assistant just for windows using python 3.6 that is ready to right to use on any windows explorer such as windows 7,8,10.we will use python as a programming language as a result of it have a major libraries which is use to implement command. Index Term: Artificial Intelligence, Desktop Assistant, Python, Text to Speech, Virtual Assistant, Voice Recognition.

AI Based Virtual Assistance on Raspberry PI

Artificial intelligence (AI) technologies are one of the new technologies with new complicated features that are emerging at a fast pace. Although these technologies seem to be extensively adopted, people do not intend to use them in some cases. Technology adoption has been studied for many years, and there are many general models in the literature describing it. However, having more customized models for emerging technologies upon their features seems necessary. In this study, we developed a conceptual model involving a new system quality construct, i.e., interaction quality, which we believe can better describe adoption of AI-based technologies. In order to check our model, we used a voice assistant system (VAS) technology as an example of this technology, and tested a theory-based model using a data set achieved from a field survey. Our results confirm that interaction quality significantly affects individual’s trust and leads to adoption of this technology. Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today's voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.

Genius – Personal Assistant

Abstract: The Genius Personal Assistant is a Voice Assistant System for laptop or desktop. As some of the physically handicapped people cannot use a laptop or mouse, keyboard properly also old age people could have problem to see the laptop’s screen. So, by using this system, the users can perform their tasks more efficiently. The user can tell to system what they want to do and that will be taken as voice input the system. After that the speech to text conversion will be performed and output according to that will be given to the user. We are implementing this project using different python libraries. Keywords: Voice Assistant System, Speech to text conversion, Python libraries.

Voice User Interface: Literature review, challenges and future directions

Natural user interfaces are increasingly popular these days. One of the most common of these user interfaces today are voice-activated interfaces, in particular intelligent voice assistants such as Google Assistant, Alexa, Cortana and Siri. However, the results show that although there are many services available, there is still a lot to be done to improve the usability of these systems. Speech recognition, contextual understanding and human interaction are the issues that are not yet solved in this field. In this context, this research paper focuses on the state of the art and knowledge of work on intelligent voice interfaces, challenges and issues related to this field, in particular on interaction quality, usability, security and usability. As such, the study also examines voice assistant architecture components following the expansion of the use of technologies such as wearable computing in order to improve the user experience. Moreover, the presentation of new emerging technologies in this field will be the subject of a section in this work. The main contributions of this paper are therefore: (1) overview of existing research, (2) analysis and exploration of the field of intelligent voice assistant systems, with details at the component level, (3) identification of areas that require further research and development, with the aim of increasing its use, (4) various proposals for research directions and orientations for future work, and finally, (5) study of the feasibility of designing a new type of voice assistant and general presentation of the latter, whose realisation will be the subject of a thesis.

Benefits and Barriers to Emerging Technologies to Promote Health, Well-Being, and Independence of Older Adults

Emerging technologies, such as voice assistant systems and artificial companion robots, hold a great deal of promise for improving the health, wellbeing, and independence of older adults. However, these solutions will likely be ineffective in the absence of research to understand barriers to the adoption and use of these technologies and without an exploration of the needs and preferences of older adults. This symposium focuses on both the potential of such technologies and factors that may affect their success. H. Spangler will present a detailed analysis of privacy concerns of older adults, with and without cognitive impairment, related to the use of Voice Assistant Systems (VAS). R. Nicholson will discuss the potential of a VAS app for promoting exercise among older adults and their caregivers to enhance mobility independence, with a focus on perceived benefits and dislikes about the app that may impact use. Finally, C. Berridge will present an exploration of perceptions of and attitudes toward artificial companion (AC) robots across the lifespan, before and after the start of the COVID-19 pandemic, including concerns about privacy. Together, these talks will highlight novel methods through which emerging technologies can support older adults and issues to consider if these methods are to produce meaningful change.

Privacy Concerns Among Older Adults Using Voice Assistant Systems

Voice Assistant Systems (VAS) are software platforms that complete various tasks using voice commands (e.g., Amazon Alexa), with increasing usage by older adults. It is unknown whether older adults have significant privacy concerns with VAS. 55 participants were evaluated from ambulatory practice sites for a study on VAS detection of early cognitive decline. The mean age was 73.3±5.6 years, 58% female, 93% white, and 53% had mild cognitive impairment. Privacy concerns were assessed via Likert-based surveys. Participants believed data was used with consent (71%) and stored properly (67%); however, 71% wanted new privacy regulations, 43% were comfortable with daily activity monitoring, and 85% thought the data needs to be highly protected. Qualitative themes included “listening-in”, “tracking”, and unwanted sharing of information. Findings suggest that older adults do not have significant privacy concerns for VAS use, but requested additional regulations. Future research can compare VAS privacy concerns between age groups.