Voice Controlled Desktop Assistant

Artificial Intelligence (AI) and Natural Language Processing (NLP) have significantly transformed human- computer interaction, enabling intelligent systems to process voice commands efficiently. Voice assistants, such as Google Assistant, Amazon Alexa, and Apple Siri, have set industry benchmarks, but they still face challenges in real-time response accuracy, handling ambient noise, and offline functionality. This paper presents the development of a custom AI -powered voice assistant, focusing on improving listening abilities, noise filtration, and command execution efficiency. The proposed system integrates Google Speech Recognition API for real-time speech-to-text conversion, pyttsx3 for text-to- speech synthesis, and natural language processing techniques to interpret and execute commands. Unlike cloud-dependent voice assistants, this system provides offline capabilities for essential commands, ensuring flexibility and usability even in low- connectivity environments. Experimental results demonstrate that the assistant achieves over 91% speech recognition accuracy in controlled environments, with an average command execution time of less than one second. Future enhancements include deep learning- based NLP models, real-time wake-word detection, and a graphical user interface for better interaction. The proposed system serves as a foundation for customizable, intelligent, and efficient AI-powered voice assistants.

In recent years, gesture recognition and speech recognition, as important input methods in Human–Computer Interaction (HCI), have been widely used in the field of virtual reality. In particular, with the rapid development of deep learning, artificial intelligence, and other computer technologies, gesture recognition and speech recognition have achieved breakthrough research progress. The search platform used in this work is mainly the Google Academic and literature database Web of Science. According to the keywords related to HCI and deep learning, such as “intelligent HCI”, “speech recognition”, “gesture recognition”, and “natural language processing”, nearly 1000 studies were selected. Then, nearly 500 studies of research methods were selected and 100 studies were finally selected as the research content of this work after five years (2019–2022) of year screening. First, the current situation of the HCI intelligent system is analyzed, the realization of gesture interaction and voice interaction in HCI is summarized, and the advantages brought by deep learning are selected for research. Then, the core concepts of gesture interaction are introduced and the progress of gesture recognition and speech recognition interaction is analyzed. Furthermore, the representative applications of gesture recognition and speech recognition interaction are described. Finally, the current HCI in the direction of natural language processing is investigated. The results show that the combination of intelligent HCI and deep learning is deeply applied in gesture recognition, speech recognition, emotion recognition, and intelligent robot direction. A wide variety of recognition methods were proposed in related research fields and verified by experiments. Compared with interactive methods without deep learning, high recognition accuracy was achieved. In Human–Machine Interfaces (HMIs) with voice support, context plays an important role in improving user interfaces. Whether it is voice search, mobile communication, or children’s speech recognition, HCI combined with deep learning can maintain better robustness. The combination of convolutional neural networks and long short-term memory networks can greatly improve the accuracy and precision of action recognition. Therefore, in the future, the application field of HCI will involve more industries and greater prospects are expected.

Human-computer interaction for virtual-real fusion

Machine learning techniques have long been the foundations of speech processing. Bayesian classiflcation, decision trees, unsupervised clustering, the EM algorithm, maximum entropy, etc. are all part of existing speech recognition systems. The success of statistical speech recognition has led to the rise of statistical and empirical methods in natural language processing. Indeed, many of the machine learning techniques used in language processing, from statistical part-of-speech tagging to the noisy channel model for machine translation have roots in work conducted in the speech fleld. However, advances in learning theory and algorithmic machine learning approaches in recent years have led to signiflcant changes in the direction and emphasis of the statistical and learning centered research in natural language processing and made a mark on natural language and speech processing. Approaches such as memory based learning, a range of linear classiflers such as Boosting, SVMs and SNoW and others have been successfully applied to a broad range of natural language problems, and these now inspire new research in speech retrieval and recognition. We have seen an increasingly close collaboration between voice and language processing researchers in some of the shared tasks such as spontaneous speech recognition and understanding, voice data information extraction, and machine translation. The purpose of this special issue was to invite speech and language researchers to communicate with each other, and with the machine learning community on the latest machine learning advances in their work. The call for papers was met with great enthusiasm from the speech and natural language community. Thirty six submissions were received; each paper was reviewed by at least three reviewers. Only ten papers were selected re∞ecting not only some of the best work on machine learning in the areas of natural language and spoken language processing but also what we view as a collection of papers that represent current trends in these areas of research both from the perspective of

Natural Language Processing (NLP) involves the use of algorithms and models and various computational techniques to analyze, process, and generate natural language data, including speech and text. NLP helps computers interact with humans in a more natural way, which has become increasingly important as more humancomputer interactions take place. NLP allows machines to process and analyze voluminous unstructured data, including social media posts, newspaper articles, reviews from customers, emails, and others. It helps organizations extract insights, automate tasks, and improve decision-making by enabling machines to understand and generate human-like language. A linguistic background is essential for understanding NLP. Linguistic theories and models help in developing NLU systems, as NLP specialists need to understand the structure and rules of language. NLU systems are organized into different components, including language modelling, parsing, and semantic analysis. NLU systems may be assessed through the use of metrics that includes measures like precision and recall, as well as indicators that convey meaningful information that include F1 score and others. Semantics and knowledge representation are central to NLU, as they involve understanding the meaning of words and sentences and representing this information in a way that machines can use. Approaches to knowledge representation include semantic networks, ontologies, and vector embeddings. Language modelling is an essential step in NLP that sees usage in applications like speech recognition, text generation, and text completion and also in areas such as machine translation. Ambiguity Resolution remains a major challenge in NLP, as language is often ambiguous and context-dependent. Some common applications of NLP include sentiment analysis, chatbots, virtual assistants, machine translation, speech recognition, text classification, text summarization, and information extraction. In this chapter, we show the applicability of a popular unsupervised learning technique, viz., clustering through K-Means. The efficiency provided by the K-Means algorithm can be improved through the use of an optimization loop. The prospects for NLP are promising, with an increasing demand for AI-powered language technologies in various industries, including healthcare, finance, and e-commerce. There is also a growing need for ethical and responsible AI systems that are transparent and accountable.

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed Artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices.

The "AI YouTube Video Summary using NLP" project introduces an innovative solution to the burgeoning challenge of digesting vast amounts of video content on platforms like YouTube. With the exponential growth of online video, users often face time constraints and information overload, hindering their ability to extract valuable insights efficiently. Our project addresses this issue by harnessing the capabilities of Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically generate concise summaries of YouTube videos. Through a seamless integration with the MERN stack, our system enables users to input video URLs and receive summaries in three distinct forms: short, long, and key insights. By automating the process of transcript extraction, linguistic analysis, and summarization, our system streamlines content consumption, offering users a time-saving and effective method for accessing essential information. By leveraging machine learning algorithms and linguistic analysis techniques, our system accurately identifies and distills key themes, concepts, and insights embedded within the video content. This empowers users to gain comprehensive understanding without the need for exhaustive viewing, thereby enhancing their browsing experience and knowledge acquisition. In essence, the "AI YouTube Video Summary using NLP" project represents a significant advancement in content consumption methodologies, offering a practical solution to the challenges posed by the proliferation of video content online. Through our innovative approach, we aim to revolutionize the way users engage with YouTube videos, facilitating efficient information extraction and empowering them to make the most of their online viewing experience. Keywords: Artificial Intelligence (AI), Natural Language Processing (NLP), Text Summarization, Multimedia Content Analysis, Automatic Summarization.

Since the launch of OpenAI's ChatGPT, the application of artificial intelligence (AI) in medical education has ignited a polarized discussion.Two main advancements will change the way we study and practice medicine, large language models (LLMs), such as generated pretrained transformer 4 (GPT-4), and natural language processing (NLP).While LLM performs language-related tasks, NLP enables machines to understand and generate human language.LLM and NLP allow neurology learners to access and synthesize information, albeit with some considerations.Medical learners struggle with an exponentially expanding medical knowledge, doubling every 73 days. 1 Internships are "training in the art of culling data." 2 Neurology's complexity, compounded by "neurophobia," underlines the need for efficacious knowledge acquisition and clinical application. 3wever, concerns arise regarding the trustworthiness of NLP-generated information.ChatGPT, for example, uses multiple sources of data sets obtained from the internet, such as Wikipedia and Reddit, wth early versions limited to knowledge up to 2021.NLP is programmed not to lie, yet these programs are known to "confabulate" and "hallucinate" to answer prompts.Learners and educators should know that NLP culls from a data set, bases recommendations from these input sources, and avoids biased decision making, but does not display inherent bias that exists in the data.AI assistance should be used carefully to protect patients' information.DocsGPT is one of the first Health Insurance Portability and Accountability Act (HIPAA)compliant AI assistance that is freely available through Doximity.On the positive side, imagine an NLP with access to all medical journals, summarizing the latest research on specific topics in minutes using the preferred medium, that is, written, audio, or video.Newer NLP tools, such as those created by John Snow Labs, are already capable of summarizing medical records and providing efficient data.NLP can also provide accurate data summarization of referral notes, charts, or laboratory results.It can write documentation, send result letters, and respond to insurance company requests.This enhances efficiency and allows neurologists and trainees to spend time with patients or in learning.The responses generated by ChatGPT are nearly indistinguishable from those provided by physicians. 4 The US Food and Drug Administration has approved 14 AI technologies for clinical neurologic applications.

ABSTRACT: In today’s highly competitive job market, effective interview preparation plays a pivotal role in achieving career success. However, conventional preparation methods often fail to provide personalization, real-time feedback, and comprehensive performance assessment. To address these challenges, this research presents the AI-Driven Interview Assistant — an intelligent, adaptive, and comprehensive AI-powered interview preparation and assessment platform. The proposed system leverages Natural Language Processing (NLP), speech analysis, and computer vision to simulate realistic interview environments while delivering actionable feedback and personalized improvement recommendations. The platform employs Large Language Models (LLMs) such as OpenAI’s GPT and Google’s Gemini for intelligent question generation and response evaluation. Real-time speech-to-text conversion using Google Speech-to-Text API or Whisper AI enables seamless voice interaction, while sentiment analysis with Hugging Face transformers evaluates emotional tone, confidence, and engagement. The core evaluation engine analyzes responses based on content relevance, communication clarity, technical accuracy, and behavioral competence. Optional computer vision modules assess facial expressions and body language for holistic performance insights. From a technical standpoint, the system is built with a React.js frontend and Node.js/Express backend, ensuring a smooth, interactive user experience. WebRTC integration enables live audio-video interview simulations, while MongoDB or PostgreSQL store detailed performance analytics for adaptive learning. The platform also integrates with job portals and company databases to deliver role-specific and industry-oriented interview preparation. Keywords – Artificial Intelligence (AI), Interview Preparation, Natural Language Processing (NLP), Large Language Models (LLMs), Speech Recognition, Whisper AI, Sentiment Analysis, Hugging Face Transformers, Computer Vision, Adaptive Learning, Real-time Feedback, Emotion Detection, React.js, Node.js, WebRTC, MongoDB, PostgreSQL, Interview Simulation, Candidate Assessment, Skill Enhancement

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance.

The advent of artificial intelligence (AI) has revolutionized human-computer interaction, making it more natural and intuitive. This research paper presents the development and implementation of an AI Desktop Voice Assistant designed to enhance productivity and accessibility for users. The voice assistant leverages advanced speech recognition, natural language processing (NLP), and machine learning techniques to understand and execute user commands. Key functionalities include voice-activated application control, web searches, personalized reminders, and real-time information retrieval. Our system integrates with widely-used APIs and services, providing a seamless user experience across various tasks. The paper discusses the architecture of the voice assistant, detailing the integration of components such as the speech recognition engine, NLP models, and the dialogue management system. We explore the challenges encountered during development, including accurate speech recognition in noisy environments and handling ambiguous user commands. Solutions implemented to address these challenges are also presented. Furthermore, we conduct a usability study to evaluate the effectiveness and user satisfaction of the voice assistant. The results indicate a high level of user engagement and satisfaction, demonstrating the practical benefits of incorporating AI-driven voice interfaces into desktop environments. Our findings contribute to the ongoing research in human-computer interaction, suggesting pathways for future enhancements in AI voice assistants..

Background: Unprecedented growth in artificial intelligence (AI) is changing the way people interact with computers- the concept known as human-computer interaction (HCI), several AI approaches like machine learning or natural language processing have boosted the user experience (UX). Nevertheless, there are some ethical concerns and differences in users’ awareness of AI systems that make it difficult to consider the general advantages of AI in HCI. Objective: Therefore, this research seeks to explore the impact of enhanced AI techniques on the level of UX in HCI with a focus on the facet of user satisfaction, trust, and usefulness of AI-based personalization techniques. It also solves the ethical problem associated with the AI systems. Methods: A quantitative research method was used with the use of structured questionnaires with 250 participants. It also captured the user engagement with AI systems, as well as their satisfaction and trust levels as well as their concerns relating to AI ethics. Descriptive analysis was used to examine the collected data through statistical tests such as; the Shapiro-Wilk test for normality and Cronbach’s Alpha for reliability. Results: It is seen that most of the users are happy with the AI technologies integrated into their lives, with special emphasis on the aspect of personalization. However, the data was nonparametric (Nomography = 0 05) and so was the internal consistency (Cronbach alpha -0. 033 on the Likert scale items were needed for survey tools of higher specific focus. The results of the survey showed that ethical considerations running across the respondents as the most significant factor of discrepancy between satisfaction and trust in areas of tension. Conclusion: In the context of AI methodologies, most are perceived to have positive effects in enhancing the UX regarding individualized engagements. However, ethical issues should be met and addressed while determining the reliability of the tools used in ascertaining the impact of AI. There is a need for future studies to address these issues and improve the techniques used for evaluating AI’s diverse position in HCI.

Campus navigation often produce challenges for students, staff, and visitors, specifically in large complex. This research presents an intelligent navigation assistant that combines automatic speech recognition (ASR), Natural Language Processing (NLP), and the A* pathfinding algorithm to tackle these issues through voice interaction. Users can naturally voice their destination queries, which the system converts into text using speech recognition techniques. Then, NLP processes the text to identify intent and relevant entities, which improves the understanding of user commands. The A* algorithm calculates the shortest path on a digital campus map and ensures effective route planning. Experimental evaluation shows high accuracy in speech-to-text conversion, effective command interpretation using NLP, and better navigation efficiency than traditional systems. This intelligent assistant improves user accessibility and convenience by providing a practical solution for hands-free campus navigation. Future work will focus on improving noise resistance, expanding language support, and integrating real-time location updates. Key Words: Intelligent Campus Navigation, Speech Recognition, Natural Language Processing (NLP), A* Pathfinding Algorithm.