Wizard based on natural language processing for Java programming language

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

The integration of Natural Language Processing (NLP) in Human-Robot Interaction (HRI) represents a significant advancement towards achieving more natural and effective communication between humans and robots. This research explores the application of state-of-the-art NLP techniques to enhance HRI, focusing on improving robots' abilities to understand and generate human language. Key components of our approach include advanced speech recognition, natural language understanding (NLU), dialogue management, and natural language generation (NLG). We designed and implemented an HRI system that leverages models such as BERT for language understanding and GPT-3 for generating contextually appropriate responses. Our methodology involves integrating these NLP models with a robotics platform, ensuring real-time interaction capabilities while maintaining a high level of accuracy and context awareness. The system was evaluated through a series of user studies, measuring performance metrics such as accuracy, latency, and user satisfaction. Results indicate that our NLP-enhanced HRI system significantly improves the quality of interactions, demonstrating superior understanding and responsiveness compared to traditional systems. This paper discusses the implementation challenges, including computational constraints and ambiguity resolution, and provides insights into user feedback and system performance. Future work will focus on enhancing context management, exploring multimodal interaction, and addressing ethical considerations in deploying advanced HRI systems. Our findings underscore the potential of NLP to transform human-robot communication, paving the way for more intuitive and effective robotic assistants in various domains. Keywords: Human-Robot Interaction (HRI), Natural Language Processing (NLP), Conversational AI, Speech Recognition, Natural Language Understanding (NLU), Natural Language Generation (NLG), Multimodal Interaction, Dialogue Systems, Context Awareness, Emotion Recognition, Machine Learning in HRI, Personalized Interaction, User Experience (UX) in HRI, Human-Centered Design, Collaborative Robots (Cobots)

Speech recognition and text summarization, plagiarism detection, machine translation, chatbots, sentiment analysis (SA), question answering (QA), and dialogue systems are all products of natural language processing (NLP), a branch of AI concerned with modeling natural languages for the purpose of developing relevant applications. NLP draws on several disciplines, not only computer science and linguistics, for its research and development. These include cognitive science, psychology, mathematics, and more. More than 1.5 billion Muslims throughout the world depend on the Arabic language for their daily five-times-prayer practice, and Arabic is one of six official languages used by more than 422 million people in the Arab world, according to UNESCO. Dialectal Arabic is the slang language spoken informally in everyday life and varies from country to country; Classical Arabic is a reflection of the language spoken by the Arabs more than fourteen centuries ago. Modern Standard Arabic is an evolving variety of Arabic that borrows and innovates regularly to meet the changing needs of its speakers. Complexity is added to the Arabic language by the fact that it encompasses not one but three different varieties of spoken language: classical, contemporary, and colloquial. However, Arabic language processing on computers remains difficult for a variety of reasons. Because of the extensive inflection and derivational processes that occur in Arabic, a single lemma may be utilized to produce several words with distinct meanings.

Introduction. The article presents an overview of modern neural network models for natural language processing. Research into natural language processing is of interest as the need to process large amounts of audio and text information accumulated in recent decades has increased. The most discussed in foreign literature are the features of the processing of spoken language. The aim of the work is to present modern models of neural networks in the field of oral speech processing.Materials and Methods. Applied research on understanding spoken language is an important and far-reaching topic in the natural language processing. Listening comprehension is central to practice and presents a challenge. This study meets a method of hearing detection based on deep learning. The article briefly outlines the substantive aspects of various neural networks for speech recognition, using the main terms associated with this theory. A brief description of the main points of the transformation of neural networks into a natural language is given.Results. A retrospective analysis of foreign and domestic literary sources was carried out alongside with a description of new methods for oral speech processing, in which neural networks were used. Information about neural networks, methods of speech recognition and synthesis is provided. The work includes the results of diverse experimental works of recent years. The article elucidates the main approaches to natural language processing and their changes over time, as well as the emergence of new technologies. The major problems currently existing in this area are considered.Discussion and Conclusions. The analysis of the main aspects of speech recognition systems has shown that there is currently no universal system that would be self-learning, noise-resistant, recognizing continuous speech, capable of working with large dictionaries and at the same time having a low error rate.

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.

In the era of digitalization, electronic gadgets such as Google Translate, Siri, and Alexa have at least one characteristic: They are all the products of natural language processing (NLP). “Natural Language” refers to a human language used for daily communication, such as English, Hindi, Bengali, etc. Natural languages, as opposed to artificial languages such as computer languages and mathematical nomenclature, have evolved as they have been transmitted from generation to generation and are challenging to explain with clear limits in the first instance. In natural language processing, artificial intelligence (Singh et al., 2021), linguistics, information processing, and cognitive science are all related fields (NLP). NLP aims to use intelligent computer techniques to process human language. However, NLP technologies such as voice recognition, language comprehension, and machine translation exist. With such limited obvious exclusions, machine learning algorithms in NLP sometimes lacked sufficient capacity to consume massive amounts of training data. In addition, the algorithms, techniques, and infrastructural facilities lack enough strength. Humans design features in traditional machine learning, and feature engineering is a limitation that requires significant human expertise. Simultaneously, the accompanying superficial algorithms lack depiction capability and, as a result, the ability to generate layers of duplicatable concepts that would naturally separate intricate aspects in forming visible linguistic data. Deep learning overcomes the challenges mentioned earlier by using deep, layered modelling architectures, often using neural networks and the corresponding full-stack learning methods. Deep learning has recently enhanced natural language processing by using artificial neural networks based on biological brain systems and Backpropagation. Deep learning approaches that use several processing layers to develop hierarchy data representations have produced cutting-edge results in various areas. This chapter introduces natural language processing (NLP) as an AI component. The history of NLP is next. Distributed language representations are the core of NLP's profound learning revolution. After the survey, the boundaries of deep learning for NLP are investigated. The paper proposes five NLP scientific fields.

In this paper, the design of a system that controls home appliances using voice commands is considered. This system may be utilized by people with special needs and the elderly. By using just their voice, they can control the lighting of their room, and turn on or off their television, air conditioner, or fan with safety and security and without needing someone else's assistance. This may be achieved by passing their voice commands to a microcontroller, (Arduino), through a device that uses voice or speech recognition technology. Herein, the system is designed at the lowest possible costs. Two basic ways are discussed to build this system, a wired system that does not depend on wireless technologies and the other depends on Bluetooth technology. Each of them has advantages and disadvantages. The hardware parts utilized in the design process are the Arduino Uno, Relay Module, and voice recognition module. The latter could be replaced by a Bluetooth module and a smartphone with an application that uses voice or speech recognition technology. The proposed wired system proved to be advantageous in terms of privacy, security and reliability. A comparison between the two systems is concluded at the end of this paper.

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.

Reviewed by: Handbook for language engineers ed. by Ali Farghaly Stuart Robinson Handbook for language engineers. Ed. by Ali Farghaly. Stanford, CA: CSLI Publications, 2002. Pp. 300. ISBN 1575863960. $25. Handbook for language engineers is a guide to the field of language processing aimed at linguists with no previous training in the area. The book contains a total of ten chapters (including the introduction). Each covers an important aspect of language processing. ‘Domain analysis and representation’ by Ali Farghaly and Bruce Hedin looks at how attention to ‘domains of discourse’ (e.g. product purchase e-mails) can improve the quality of natural language processing (NLP). ‘The language of the internet’ by Naomi S. Baron provides an overview of the language of the internet in the broadest sense, covering the unique character of natural language on the internet as well as other issues, such as coding systems (mark-up languages), search engines, and crosslinguistic issues. ‘Grammar writing, testing, and evaluation’ by Miriam Butt and Tracy Holloway King gives an overview of various aspects of grammar implementation, focusing on development, testing, evaluation, and documentation. ‘Ontologies’ by Natalya Noy discusses the role of ontologies in language [End Page 679] engineering, where the term ontology is understood to mean ‘an explicit formal specification of conceptualization’ (Thomas R. Gruber, ‘A translation approach to portable ontology specifications’, Knowledge Acquisition 5.2.199–220, 1993). ‘Text mining, corpus building, and testing’ by Karine Megerdoomian looks at the role of corpora in language engineering. She discusses the nature of corpora, touching on a wide variety of topics (corpus analysis vs. text mining, tokenization and annotation, the application of corpus linguistics, tools for corpus processing, symbolic and statistical approaches). ‘Statistical natural language processing’ by Chris Callison-Burch and Miles Osborne discusses the role of statistics in NLP, beginning with a discussion of probabilistic parsing, then moving on to modeling, estimation/learning, data, and evaluation. Matthew Stone (‘Knowledge representation for language engineering’) examines how world knowledge is represented in language processing. The discussion is broadly divided into three sections, each of which covers three layers of analysis in intelligent systems: the knowledge level, the representations-and-algorithms level, and the implementation level. In ‘Speech recognition and understanding’, Jan W. Amtrup looks at the state of the art in speech recognition and speech synthesis, focusing on some of the statistical methods that have been successfully employed in the field, such as hidden Markov models. Ali Farghaly wraps up the discussion in ‘Language engineering and the knowledge economy’ by looking at the changing economics of globalization and the place of language engineering in the new economy. The growth of the internet has created many jobs for linguists in industry and raised the profile of computational linguistics. This book should serve as an excellent guide for those interested in a practical introduction to the current state of language processing technology. Stuart Robinson Max Planck Institute for Psycholinguistics Copyright © 2006 Linguistic Society of America

The AI-Powered Deaf Companion System is designed to bridge the communication gap between deaf and hearing individuals using cutting-edge deep learning technologies. Leveraging Temporal Convolutional Networks (TCNs), the Sign Recognition Module (SRM) accurately interprets sign language gestures in real time. Meanwhile, the Speech Recognition and Synthesis Module (SRSM), powered by Hidden Markov Models (HMMs), converts spoken language into text, ensuring seamless bidirectional communication. To enhance accessibility, the Avatar Module (AM) visually translates spoken text into sign language gestures, making interactions more intuitive. By integrating computer vision, natural language processing (NLP), and speech synthesis, this system fosters inclusive communication, empowering the deaf community with greater independence and interaction opportunities.

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.

This paper describes a web application that was designed to prepare and process speech corpora, key data sources for automatic speech recognition (ASR), natural language processing (NLP), speech synthesis (TTS) and many other tasks. The application allows users to process the corpora with no other equipment than a web browser with internet connection. The application has been used, upgraded and improved for several years and its history is also described here. During that time, many valuable experiences with speech corpora processing have been gained and they are also mentioned as some good practices.

The paper presents a system for speech recognition and synthesis for the Kazakh and Russian languages. It is designed for use by speakers of Kazakh; due to the prevalence of bilingualism among Kazakh speakers, it was considered essential to design a bilingual Kazakh-Russian system. Developing our system involved building a text processing and transcription system that deals with both Kazakh and Russian text, and is used in both speech synthesis and recognition applications. We created a Kazakh TTS voice and an additional Russian voice using the recordings of the same bilingual voice artist. A Kazakh speech database was collected and used to train deep neural network acoustic models for the speech recognition system. The resulting models demonstrated sufficient performance for practical applications in interactive voice response and keyword spotting scenarios.

Mark Watson Morgan Kaufmann Publishers, San Francisco, 1997, 377 pp. ISBN 1-55860-420-0, $44.95 This book is well written and organized to cover how to apply AI (Artificial Intelligence) techniques for practical Internet and Intranet tools by using the Java programming language. In the book, the author uses neural network and genetic algorithms to develop distributed systems that are applied for the Internet and Intranets, such as client/server model and information retrieval tool and so on. Part 1 begins with a good general introduction to AI. The history and survey of Artificial Intelligence research are given briefly. In addition, symbolic processing versus neural networks approaches are discussed. This author opts to use neural network to develop example Internet/Intranet systems in the book. Part 2 consists of Java programs developed for AI, neural networks, genetic algorithms, natural language processing, and expert systems. Chapter 2 illustrates Graphical User Interface (GUI) class that is derived from Applet and Canvas classes in Java, which is used in most example programs in the book. Chapter 3 introduces a frame data structure that is used as a message in distributed systems in the later chapters. Chapter 4 shows neural network classes developed in Java and its theory and implementation. Chapter 5 explains the genetic algorithms and the Java genetic algorithm classes. Chapter 6 describes how to develop and implement natural language processing classes using Java. Chapter 7 shows how to share data objects in distributed systems. For example, distributed AI agent classes are developed to share data objects, WWW data collection agent classes are shown to retrieve documents in the Internet, and small POP (Post Office Protocol) mail classes are developed to send and receive mails. Chapter 8 illustrates how to develop expert systems by using Java Jess, which is based on OPS5/CLIPS expert systems. Part 3 explains how to generate and implement the projects using the Java classes that are developed in Part 2. Chapter 9 shows the project that demonstrates the use of genetic algorithms in chapter 5 to control enemy ships in a scrolling arcade game. Chapter 10 describes the real-time handwriting recognition system developed by using the neural network classes in chapter 4. Chapter 11 illustrates a greedy algorithm to optimize neural networks that is based on the author’s hand-tuning neural training data. Chapter 12 improves neural network training data by applying genetic algorithms, to discard bad training data and to avoid discarding good training data. Chapter 13 describes the distributed natural language processing system that uses natural language processing classes in chapter 6. The data structures in chapter 2 are used to store historical data in a separate server application on any other computer in the Internet or Intranets. The final chapter provides the design and implementation of a complete agent-based information retrieval tool for the Internet. The retrieved documents are stored in ASCII or HTML format. This book is a very good reference book for anyone interested in developing systems using Java in distributed environments, especially applying AI techniques. Also, the author provides the CD-ROM that includes the source code for Java classes developed in the book. Haklin Kimm, University of Tennessee

Most door accesses used in homes still use conventional keys. Where the use of manual keys is still vulnerable to loss or damage and is not very safe to use, because manual keys can be duplicated or duplicated by other people. This certainly feels less practical and flexible. This research aims to help improve door security. With a voice recognition system, only certain people can access it using a certain access code. Researchers used the trial and error method. The factor being tried is the voice recognition V3 module which can read voice recordings. The results of this research can be implemented in the form of a tool, where the system can open and close house doors according to the planned design, namely through voice commands and the designed system can run normally according to the automatic system that has been designed in prototype form. The conclusion from this research is that the voice recognition module is capable of recording sounds up to a distance of 20 cm. In voice recording < 10 cm the module is relatively easier to receive voice commands, in recording > 10 cm and < 15 cm the module is slightly less able to receive voice commands, so that in voice recording at a distance of 15 cm to 20 cm the module is very poor in receiving commands sound well.