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Natural Language Processing And Voice Control Bring Conversational Robots Closer To Reality
(MENAFN- Robotics & Automation News) Natural language processing and voice control for robots: The conversational robotAugust 29, 2025 by Mai Tao
Robots are steadily becoming easier to interact with. For decades, programming a robot meant learning a proprietary interface or mastering complex code.
Now, thanks to advances in natural language processing (NLP) and voice control, operators can instruct machines using everyday speech.
This shift promises to make robots more accessible to non-technical users, expand their applications, and ultimately accelerate adoption across industries.
From command lines to conversationTraditional robot programming relied on explicit, structured commands – often in a scripting language designed for engineers. This created a steep learning curve, and in many cases, programming time was a significant share of a robot's total cost of deployment.
Recent breakthroughs in NLP have changed the equation. By allowing robots to parse human language, engineers can issue instructions like"Pick up the blue container and place it on the third shelf" rather than writing lines of code. Even more importantly, NLP can interpret variations in phrasing, enabling flexibility in communication.
Voice interfaces add another layer of convenience. In situations where operators have their hands full – in warehouses, construction sites, or healthcare – spoken instructions are faster and more natural than navigating a screen.
How NLP works in roboticsNLP combines several AI disciplines, including speech recognition, syntactic parsing, and semantic understanding. In robotics, the process typically follows three stages:
This pipeline allows robots to respond to requests that are more natural and less rigidly structured than traditional programming languages.
Where voice-controlled robots are making an impact Manufacturing and logisticsIn factories and warehouses, workers can direct robots without interrupting their workflow. For example, a logistics operator might tell a mobile robot,"Bring me pallet number 14 from aisle C," while continuing another task.
HealthcareService robots in hospitals can respond to requests from staff or patients, such as delivering supplies or guiding visitors. Voice control reduces the need for physical interfaces, improving hygiene and accessibility.
Hospitality and retailRobotic assistants in hotels or stores can greet customers, answer questions, or take orders in natural language. This not only improves customer engagement but also reduces the need for human staff in repetitive roles.
Home roboticsSmart home robots – from vacuum cleaners to personal assistants – are already equipped with voice controls through platforms like Amazon Alexa, Google Assistant, and Apple Siri.
Technical challenges and limitationsWhile NLP and voice control have made great strides, several challenges remain:
The integration of large language models (LLMs) into robotics is pushing conversational abilities forward. By leveraging models similar to those powering advanced AI chatbots, robots can handle more complex interactions, infer context, and even ask clarifying questions.
Another area of active development is speech-to-action learning, where robots directly map spoken commands to physical actions using reinforcement learning. This could eliminate some of the intermediate programming layers, making interaction even faster.
Leading companies and research groupsSeveral companies and institutions are pioneering NLP and voice interfaces for robotics:
Among the most promising developments in conversational robotics is the emerging combination of OpenAI's advanced language models with Figure AI's humanoid platforms.
While large tech companies such as Apple (Siri), Google (Google Voice), and Microsoft (Cortana) introduced voice recognition into mainstream devices years ago, these systems have often struggled to accurately interpret speech for some users, especially in more nuanced or free-form conversations.
In contrast, OpenAI's voice-enabled models – already deployed in ChatGPT's mobile app – have shown significantly greater flexibility and accuracy in real-time dialogue. For certain users, including those with less conventional speech patterns, this has translated into a markedly better experience compared with earlier voice assistants.
When paired with Figure AI's humanoid robot, the result could be a step change in practical, voice-driven robotics. Figure's most recent viral demonstration, covered by Robotics & Automation News earlier this month, showed its Figure 02 robot autonomously performing laundry tasks – picking garments from a basket and loading them into a washing machine – using its Helix Vision-Language-Action model.
Although it did not yet operate the washer's controls, the dexterity and autonomy displayed marked a tangible milestone in household robotics.
Integrating a conversational AI system capable of understanding complex, spoken instructions with a humanoid robot able to physically execute them could accelerate the arrival of all-purpose, voice-controlled household robots.
While widespread commercial availability is still some distance away, this pairing may represent one of the most direct routes toward robots that operate in ways familiar from science fiction: understanding, responding, and acting with human-like fluency.
The road aheadAs NLP becomes more sophisticated, conversational robots will transition from following direct commands to collaborating more like human teammates. In the near term, expect voice-controlled robots to be increasingly common in logistics, healthcare, and service industries, where speed, accessibility, and hands-free operation deliver immediate value.
In the long term, the vision is for robots that can truly understand nuance, context, and intent – engaging in back-and-forth dialogue to clarify instructions and even suggest improvements to a task. This human-like interaction could be the catalyst that makes robotics a universal tool, not just for engineers, but for everyone.
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5 Natural Language Processing Libraries To Use - Cointelegraph
Natural language processing (NLP) is important because it enables machines to understand, interpret and generate human language, which is the primary means of communication between people. By using NLP, machines can analyze and make sense of large amounts of unstructured textual data, improving their ability to assist humans in various tasks, such as customer service, content creation and decision-making.
Additionally, NLP can help bridge language barriers, improve accessibility for individuals with disabilities, and support research in various fields, such as linguistics, psychology and social sciences.
Here are five NLP libraries that can be used for various purposes, as discussed below.
NLTK (Natural Language Toolkit)One of the most widely used programming languages for NLP is Python, which has a rich ecosystem of libraries and tools for NLP, including the NLTK. Python's popularity in the data science and machine learning communities, combined with the ease of use and extensive documentation of NLTK, has made it a go-to choice for many NLP projects.
NLTK is a widely used NLP library in Python. It offers NLP machine-learning capabilities for tokenization, stemming, tagging and parsing. NLTK is great for beginners and is used in many academic courses on NLP.
Tokenization is the process of dividing a text into more manageable pieces, like specific words, phrases or sentences. Tokenization aims to give the text a structure that makes programmatic analysis and manipulation easier. A frequent pre-processing step in NLP applications, such as text categorization or sentiment analysis, is tokenization.
Words are derived from their base or root form through the process of stemming. For instance, "run" is the root of the terms "running," "runner," and "run." Tagging involves identifying each word's part of speech (POS) within a document, such as a noun, verb, adjective, etc.. In many NLP applications, such as text analysis or machine translation, where knowing the grammatical structure of a phrase is critical, POS tagging is a crucial step.
Parsing is the process of analyzing the grammatical structure of a sentence to identify the relationships between the words. Parsing involves breaking down a sentence into constituent parts, such as subject, object, verb, etc. Parsing is a crucial step in many NLP tasks, such as machine translation or text-to-speech conversion, where understanding the syntax of a sentence is important.
Related: How to improve your coding skills using ChatGPT?
SpaCySpaCy is a fast and efficient NLP library for Python. It is designed to be easy to use and provides tools for entity recognition, part-of-speech tagging, dependency parsing and more. SpaCy is widely used in the industry for its speed and accuracy.
Dependency parsing is a natural language processing technique that examines the grammatical structure of a phrase by determining the relationships between words in terms of their syntactic and semantic dependencies, and then building a parse tree that captures these relationships.
Stanford CoreNLPStanford CoreNLP is a Java-based NLP library that provides tools for a variety of NLP tasks, such as sentiment analysis, named entity recognition, dependency parsing and more. It is known for its accuracy and is used by many organizations.
Sentiment analysis is the process of analyzing and determining the subjective tone or attitude of a text, while named entity recognition is the process of identifying and extracting named entities, such as names, locations and organizations, from a text.
GensimGensim is an open-source library for topic modeling, document similarity analysis and other NLP tasks. It provides tools for algorithms such as latent dirichlet allocation (LDA) and word2vec for generating word embeddings.
LDA is a probabilistic model used for topic modeling, where it identifies the underlying topics in a set of documents. Word2vec is a neural network-based model that learns to map words to vectors, enabling semantic analysis and similarity comparisons between words.
TensorFlowTensorFlow is a popular machine-learning library that can also be used for NLP tasks. It provides tools for building neural networks for tasks such as text classification, sentiment analysis and machine translation. TensorFlow is widely used in industry and has a large support community.
Classifying text into predetermined groups or classes is known as text classification. Sentiment analysis examines a text's subjective tone to ascertain the author's attitude or feelings. Machines translate text from one language into another. While all use natural language processing techniques, their objectives are distinct.
Can NLP libraries and blockchain be used together?NLP libraries and blockchain are two distinct technologies, but they can be used together in various ways. For instance, text-based content on blockchain platforms, such as smart contracts and transaction records, can be analyzed and understood using NLP approaches.
NLP can also be applied to creating natural language interfaces for blockchain applications, allowing users to communicate with the system using everyday language. The integrity and privacy of user data can be guaranteed by using blockchain to protect and validate NLP-based apps, such as chatbots or sentiment analysis tools.
Related: Data protection in AI chatting: Does ChatGPT comply with GDPR standards?
Best NLP (Natural Language Processing) Books To Read In 2025
Overviews
Explore the best NLP books of 2025 to master AI, ML, and deep learning concepts.
From classics to modern guides, discover NLP resources shaping tomorrow's AI world.
Learn theory, practice, and trends with the most trusted NLP books in 2025.
The year 2025 has ushered us into the era of NLP, one of the most trending fields of recent times. This has been fueled by large language models, deep learning, and the development of multilingual understanding. It can be hard for one to understand all the new methodologies, theories, and their applications. However, if one is interested, a grounding resource, such as a book, is of great help.
Whether you're a student, researcher, developer, or data professional, you can learn and understand new concepts properly with the help of good material. Here are the best NLP books to read in 2025.
Speech and Language Processing by Daniel Jurafsky & James H. Martin
This text is the most often referenced and respected in the NLP community. It is used in undergraduate and graduate courses and is as relevant as ever. The third edition of the book is still forthcoming, but it will contain everything necessary for building a strong academic or professional foundation.
This NLP book is equally beneficial for both academic and professional users due to its balanced approach to theory and practice. In addition to language modeling, parsing, and semantics, it also covers modern neural methods.
Its second edition is widely used in undergraduate and graduate courses, is as relevant as ever, and the third edition is eagerly awaited. This second edition is widely used and remains as relevant as ever; the third edition is eagerly anticipated.
Natural Language Understanding by James Allen
The classic that goes back to 1994, this book is a wonderful collection of core NLP concepts and techniques. It remains in strong and active use due to its distinctive collection. For instance, if you want to gain a foundational understanding of how NLP systems actually work, this is the best and most trusted resource that still utilizes strong conceptual frameworks, without relying on trendy buzzwords.
Also Read: Best Books to Learn Forex Trading in 2025
Handbook of Natural Language Processing by Nitin Indurkhya & Fred J. Damerau
This guide is organized into three straightforward parts: classical symbolic methods, stochastic methods, and applications (for instance, sentiment analysis and text mining across languages).
The second edition of the Handbook of Natural Language Processing expands its coverage to include many European and Asian languages, and strengthens its focus on statistical methods and new applications - a valuable resource for developers and engineers who will apply NLP methods in various contexts.
The Handbook of Computational Linguistics and Natural Language Processing by Alexander Clark, Chris Fox & Shalom Lappin
Bridging theoretical physics and engineering, The Handbook of Computational Linguistics and Natural Language Processing, by Alexander Clark, Chris Fox & Shalom Lappin, fine-tunes the introduction to computational linguistics and NLP core topics, striking an equilibrium between theory, techniques, and engineering practices.
The book is suitable for students or specialists who require a strong theoretical foundation and practical application in mind.
The Oxford Handbook of Computational Linguistics by Ruslan Mitkov (Ed.)
With the explanation of well-known NLP academic Ruslan Mitkov, this volume provides a concise introduction to linguistic fundamentals and a simplified overview of new tasks, tools, and methods in NLP and computational linguistics.
This has made this book one of the best NLP books in 2025, primarily for undergraduates and generalists. It is a helpful resource, primarily for individuals with a background in linguistics, artificial intelligence, or cognitive science.
Which One Offers the Best Experience?
Each of the books mentioned above is good, but in 2025, for a rewarding read, one must choose Speech and Language Processing by Jurafsky & Martin. No other book comes close to balancing depth of coverage, pedagogical clarity, and up-to-date relevance. The content is well-organized, the tone of the book is inviting, and it is regularly revised to include the latest NLP breakthroughs. It's unrivaled in that regard.
What further sets it apart is the practical use of this book. Most NLP courses are centered on the book, with its exercises and case studies collaboratively refined and tested. This book stands out in a class of its own on this list, satisfying the needs of both theory and practice, while also offering a firm conceptual grasp.
Also Read: 10 Essential Books for Mastering Data Visualization
Final Thoughts
As far as NLP is concerned, networks of dated, high-quality texts remain the most dependable sources. These NLP books are the most suitable choices because they include classic introductory details of concepts, cover core ideas as well as new methods. For grasping foundational ideas, check out Natural Language Understanding by James Allen. For coverage of methods in multiple languages, look at the Handbook of NLP or the Oxford Handbook.
If you are more interested in the statistical side, consider taking Foundations of Statistical NLP. If you need a book that combines linguistics and engineering, don't miss out on the Clark-Fox-Lappin book. However, first prioritize what you need and look for a book that suits those requirements.
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FAQs:
1. What are the best NLP books to start with in 2025?
Ans: Some top beginner NLP books include Natural Language Processing Crash Course for Beginners, Natural Language Processing in Action, and Foundations of Statistical Natural Language Processing. These are praised for hands-on coding, clear theory, and practical examples.
2. Which NLP book is best for practical projects?
Ans: Natural Language Processing in Action is highly recommended as it provides numerous step-by-step code examples and practical projects, including building chatbots and search engines.
3. Are there NLP books with Python code for 2025?
Ans: Yes, books like Natural Language Processing Crash Course for Beginners focus on NLP using Python and libraries like TensorFlow and Keras, ideal for implementing techniques.
4. What advanced NLP books should professionals read?
Ans: For deep theoretical and mathematical understanding, Foundations of Statistical Natural Language Processing by Manning and Schütze is the classic advanced text, used by both researchers and practitioners.
5. What topics do top NLP books cover in 2025?
Ans: The best NLP books cover topics like text preprocessing, word embeddings, deep learning for NLP, sentiment analysis, named entity recognition, and real-world NLP applications in fields like search and chatbots.
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