Machine Translation has become one of the major application areas of Artificial Intelligence (AI) and Natural Language Processing (NLP), especially in multilingual countries like India. Although recent Neural Machine Translation systems have shown good performance for several language pairs, translation quality is still inconsistent for many Indian languages because of linguistic and structural differences between English and Indian language families. Most Indian languages are morphologically rich and contain flexible word order, complex agreement patterns, compound constructions, and context-dependent grammatical forms. Because of this, direct translation from English often produces structurally incorrect or semantically weak output. In many existing systems, the source sentence is passed to the translation model without sufficient linguistic analysis. As a result, ambiguity present in the source text propagates further during translation. This work focuses on the importance of linguistic enrichment before the translation stage. The proposed framework, named Unified Linguistic-Aware Pre-Parsing Framework, introduces a coordinated pre-processing layer for English-to-Indian Machine Translation (MT). A key contribution of this research is the development of a novel linguistically enriched intermediate representation that extends beyond conventional text normalization. By transforming noisy input text into linguistically enriched translation-ready representation, the proposed approach facilitates effective knowledge transfer to machine translation models, leading to improve contextual adequacy, linguistic fidelity, and overall translation performance. The framework combines multiple linguistic processing stages including POS tagging, NE detection, clause boundary analysis, contextual token handling, syntactic structure preparation, and morphology-related processing. Instead of executing these modules independently, the proposed system allows interaction between lexical, syntactic, and morphological information during analysis. This helps reduce structural ambiguity and improves sentence-level interpretation before translation begins. The need for such a framework becomes more relevant in the context of Indian languages where morphology and grammatical relations carry significant semantic information. This framework is especially relevant for Indian languages, where semantic information is often encoded through morphological variations and grammatical dependencies. The proposed framework can be effectively integrated with both conventional machine translation architectures and modern large language models. The overall study highlights how classical linguistic analysis can still play an important role in improving multilingual AI systems for Indian languages.

This paper presents an extended statistical parsing framework for Tree-Adjoining Grammar (TAG) that incorporates part-of-speech (POS) information to enhance syntactic disambiguation, improve accuracy, and increase cross-lingual adaptability. While TAG provides a linguistically expressive mechanism for representing complex syntactic phenomena such as recursion and long-distance dependencies, however, conventional statistical TAG parsers remain largely constrained by their reliance on lexical anchors, which limits generalization across languages and leads to inefficiencies in ambiguous contexts. To address this, we improvise the statistical TAG formalism by conditioning derivation decisions on both lexical items and their associated POS tags, thereby enriching the feature space with syntactic category information. Beyond the baseline framework, this extended version introduces three major contributions. First, it integrates POS-based features into both generative and discriminative models, enabling robust handling of unseen or low-frequency lexical items. Second, it presents a cross-lingual evaluation using multilingual treebanks covering English to Indian language pairs, demonstrating consistent improvements in parsing accuracy and a 40–45% reduction in parsing time compared to conventional lexicalized TAG parser. Third, it provides an expanded analysis of computational efficiency, error patterns, and scalability across varying sentence lengths and linguistic families. Experimental results on a dataset of 15,000 annotated sentences reveal that the latest parser achieves significant gains in both accuracy and efficiency, with stable performance even in low-resource scenarios. The framework’s design further allows integration with neural embeddings, opening pathways toward hybrid symbolic–neural parsing models. Overall, the proposed POS enriched cross-lingual TAG framework offers a scalable, linguistically grounded, and computationally efficient solution for modern Natural Language Processing (NLP) tasks, including machine translation, information extraction, and question answering.

As the field of Automatic Speech Recognition (ASR) advances, multilingual and multitask models are becoming essential for supporting diverse languages. Models like Whisper have made significant progress in this direction, enabling speech-related tasks such as transcription and translation across 99 languages. However, Whisper's performance remains limited by the availability of training data and linguistic diversity, often underperforming on underrepresented languages. Larger Whisper variants, while effective, are computationally expensive, whereas smaller variants, though efficient, struggle with Indian languages in particular. In this paper, we present Dhwani (Domain-adapted Hybrid Whisper for Audio transcription in Indic languages), a continual pretraining framework based on the foundational Whisper model to enhance coverage and performance for Indian languages. Dhwani extends Whisper's language support to new low-resource languages demonstrated here for Odia and can be generalized to others. The model is trained in a multilingual and multitask setup with Language Identification (LID) and Automatic Speech Recognition (ASR) as joint learning objectives, incorporating on-the-fly audio augmentation for regularization. we employ a continual pretraining strategy with partial encoder freezing and two-learning-rate optimization to enhance recognition accuracy while maintaining model efficiency. Our model supports 12 Indian languages and demonstrates significant improvements over baseline Whisper variant. The complete training recipe and model weights will be open-sourced in the future to facilitate reproducible research in multilingual and low-resource ASR.

This paper presents a novel probabilistic parsing framework for Tree-Adjoining Grammar (TAG) that integrates part-of-speech (POS) information to enhance syntactic disambiguation and improve parsing accuracy. While TAG remains a linguistically expressive formalism for modelling complex syntactic phenomena such as long-distance dependencies and recursive structures, conventional statistical TAG parser predominantly relies on lexical information, limiting their ability to resolve structural ambiguities inherent in Natural Languages (NL). To address this limitation, we extend the probabilistic TAGformalism by conditioning derivation decisions jointly on lexical anchors and their associated POS tags. Our model supports both generative and discriminative formulations, incorporating POS-based feature representations into the derivation scoring mechanism. The training process is adapted to align POS-tagged lexical items with elementary tree structures, allowing the parser to learn syntactic patterns with greater accuracy and robustness. Empirical evaluations across multiple languages demonstrate that POS- augmented approach yields significant gains in parsing accuracy, particularly in the presence of syntactic ambiguity. The POS-Augmented Statistical Parser was evaluated on a dataset of 12,000 sentences, resulting a 30% reduction in parsing time compared to the conventional TAG Parser. The integration of POS not only enhances parsing speed but also provides structural advantages. In contrast, Tree Adjoining Grammar (TAG) often struggles to fully capture the complexity of linguistic phenomena, especially in crosslinguistic transfer between English and Indian languages. The proposed framework of ers a scalable and linguistically informed enhancement to TAG-based systems, bridging symbolic grammatical representations with data-driven statistical learning. This POS-augmented approach of ers a lightweight yet ef ective extension to existing TAG-based systems, enhancing their linguistic expressiveness and robustness for Natural Language Processing (NLP) applications.

In the age of Artificial Intelligence (AI), a significant breakthrough occurred as machines demonstrated their ability to communicate in human languages. This marked the beginning of a ground-breaking era in Natural Language Processing. defined by unparalleled computational capabilities. Amidst this evolution, parsers stand as an indispensable component, facilitating syntactic comprehension and empowering various NLP applications, from Machine Translation to sentiment analysis. Parser plays a crucial role in deciphering the complex syntactic structures inherent in human languages. With the use of a parser, machines can comprehend human language, extract meaning, and facilitate a variety of natural language processing (NLP) applications, such as information retrieval, sentiment analysis, and machine translation. This research paper presents the implementation of Bi-Directional Head-Driven Parser, aiming to expand the horizons of NLP beyond the constraints of traditional early-type L-TAG (Lexicalized Tree Adjoining Grammar) Parsing. While effective, conventional Parsers encounter inherent limitations in grappling with the intricacies and subtleties of natural language. Through the utilization of Bi-Directional principles, Head-Driven techniques offer a revolutionary breakthrough in computational frameworks for large-scale grammar parsing, enabling complex NLP tasks such as discourse analysis and semantic parsing, and guaranteeing reliable linguistic analysis for practical applications. The performance of the Bi-Directional Parser has been examined on the data set of 15000 sentences and observed a reduction in the variation of derivations for sentences of the same length compared to the conventional TAG Parser, this research showcases how Head-Driven Parser facilitates breakthrough in language processing, syntactic analysis, semantic comprehension, and beyond. Moreover, it underscores the structural implications of integrating Head-Driven Parsing. Traditional approaches, such as Tree Adjoining Grammar (TAG), while valuable, often encounter limitations in capturing the full spectrum of linguistic phenomena, particularly in the context of cross-linguistic transfer between English and Indian languages. In light of the significance of natural language processing (NLP) in addressing these issues, this research introduces a Bi-Directional Head-Driven Parser implementation. Drawing upon the rich foundation of TAG and acknowledging its constraints, our approach transcends these limitations by harnessing advanced parsing traversal techniques and linguistic theories. By bridging the gap between theory and application, our approach not only enhances our understanding of syntactic parsing across language families but also surpasses the performance of an ‘Early-type Parser’ in terms of time and memory. Through rigorous experimentation and evaluation, this research contributes to the ongoing discourse on expanding the frontiers of Tree Adjoining Grammar-based research and shaping the trajectory of Machine Translation.

The agriculture sector faces significant challenges due to the limited adoption of efficient practices by farmers, leading to reduced crop yields and quality. This research introduces a specialized chatbot tailored for agricultural inquiries, leveraging advanced technologies such as Large Language Models integrated with Knowledge Graphs and vector databases. The chatbot performs thorough query analysis and delivers personalized information, facilitated by a user-friendly interface that supports seamless interaction, effective query categorization, and clear responses. Additionally, it incorporates voice functionality to improve accessibility for users with learning disabilities or literacy challenges. Unlike existing solutions, this chatbot is uniquely designed for agriculture, offering free, portable, and reliable guidance on farming techniques and related topics. By empowering farmers to make informed decisions about crop selection, pest management, and irrigation methods, the system aims to promote sustainable farming practices and enhance financial outcomes. The research methodology includes data preparation, model selection, and the implementation of a knowledge graph memory base. The development of APIs and databases ensures effective communication and user engagement, with comprehensive testing validating the system’s functionality, performance, and accuracy in diverse scenarios. This paper highlights the potential of advanced technologies in creating specialized agricultural chatbots, providing a promising approach to disseminate agricultural knowledge and support stakeholders in the sector.

In the era of Artificial Intelligence (AI), significant progress has been made by enabling machines to understand and communicate in human languages. Central to this progress are parsers, which play a vital role in syntactic analysis and support various Natural language Processing (NLP) applications, including Machine Translation and sentiment analysis. This paper introduces a robust implementation of an optimized Head-Driven Parser designed to advance NLP capabilities beyond the limitations of traditional Lexicalized Tree Adjoining Grammar (L-TAG) based Parser. Traditional parser, while effective, often struggle with the capturing complexities of natural languages, especially translation between English to Indian languages. By leveraging Bi-directional approach and Head-Driven techniques, this research offers a revolutionary enhancement in parsing frameworks. This method not only improves performance in syntactic analysis but also facilitates complex tasks such as discourse analysis and semantic parsing. This research involves experimentation the Bi-Directional Parser on a dataset of 15,000 sentences, resulting a reduction in derivation variations compared to conventional TAG Parsers. This advancement highlights how Head-Driven Parsing can overcome traditional constraints and provide more reliable linguistic analysis. The paper demonstrates how this new implementation not only builds on the strengths of L-TAG but also addresses its limitations and contributes to expanding the scope of Tree Adjoining Grammarbased methodologies and advancing the field of Machine Translation.

This paper presents a state-of-the-art virtual research lab (vTAG) for creating, updating, analyzing, and maintaining multilingual large-scale tree-adjoining grammar for natural languages (NL). vTAG has designed to be language independent and tested its performance by constructing grammar of many Indian and European languages with significant reductions in grammar development time by auto rule creation mechanism with the help of a supervised machine learning algorithm. vTAG provides an integrated development environment (IDE) that can generate grammar of any natural language based on tree-adjoining grammar (TAG) formalism by using a specially designed graphical user interface without focusing on the programming aspect. vTAG also contains an advanced age experimental workspace to evaluate and improvise TAG-based parsers, part-of-speech tagger, sematic text analyzer, and associated NLP tools. vTAG can be utilized as an ‘interactive training kit’ for upcoming young talent to empower them for learning and developing advanced tools solving practical problems in NLP field. Furthermore, it can serve as foundation for understanding and building end-to-end machine translation solutions. vTAG uses object oriented model (OOM) for handling the NLP resources so every entity within the environment is in form of an object that can be encrypted which makes linguistic resources convenient for maintenance and secure to exchange and distribution, thus vTAG consists of a workbench, development kit, a graphical user interface, experimental workspace, and language resources on a single platform by combining major concepts from modern computer science, artificial intelligence, and linguistics.

The Revolution of the Artificial Intelligence (AI) has started when machines could decipher enigmatic symbols concealed within messages. Subsequently, with the progress of Natural Language Processing (NLP), machines attained the capacity to understand and comprehend human language. Tree Adjoining Grammar (TAG) has become powerful grammatical formalism for processing Large-scale Grammar. However, TAG mostly rely on Grammar which is created by Languages expert and due to structural ambiguity in Natural Languages computation complexity of TAG is very high o(n^6). We observed that rules-based approach has many serious flaws, firstly, language evolves with time and it is impossible to create grammar which is extensive enough to represent every structure of language in real world. Secondly, it takes too much time and language resources to develop a practical solution. These difficulties motivated us to explore an alternative approach instead of completely rely on the rule-based method. In this paper, we proposed a Statistical Parsing algorithm for Natural Languages (NL) using TAG formalism where Parser makes crucial use of data driven model for identifying Syntactic dependencies of complex structure. We observed that using probabilistic model along with limited training data can significantly improve both the quality and performance of TAG Parser. We also demonstrate that the newer parser outperforms previous rule-based parser on given sample corpus. Our experiment for many Indian Languages, also provides further support for the claim that above mentioned approach might be an awaiting solution for problem that require rich structural analysis of corpus and constructing syntactic dependencies of any Natural Language without much depending on manual process of creating grammar for same. Finally, we present result of our on-going research where probability model will be applying to appropriate selection of adjunction of any given node of elementary trees and state chart representations are shared across derivation.

Speaker recognition is a prominent area of study within speech technology. In recent times, there has been a notable transition towards embedding-based end-to-end speaker recognition approaches. These methods enable speaker recognition without the need for retraining when encountering new speakers in real-world scenarios. Earlier models were trained on limited monotonous hand-crafted datasets, which worked on pattern matching, but failed to be robust because of architectural limitations and insufficient data. Research shows that training deep neural networks, which rely on attention mechanisms, on extensive datasets containing weakly labeled and diverse samples can significantly improve their resilience when applied to various classification tasks downstream. Drawing upon knowledge acquired from extensive datasets like VoxCelebl and VoxCeleb2, along with specially crafted proprietary data tailored to handle various modalities, we initiated an investigation into the capabilities of these versatile techniques for building speaker recognition models. Our attention was directed towards two reputable models, ECAPA-TDNN [1](Emphasized Channel Attention, Propagation, and Aggregation) and Titanet[2], as we aimed to comprehend the architectural enhancements and assess their impact on the model's ability to generalize effectively. Also, we propose a faster and scalable inference pipeline using Elasticsearch.

For developing and using various features provided by the internet by all the sections of the society, it is important to make the technology accessible irrespective of the language. So, the translation between the languages becomes important criterion. Previously, the machine translation has been used to translate between different languages, which eased the communication between people from different linguistic backgrounds. But in the last couple of years, the advancement and enhancement of Deep Neural Network (DNN) allowed us to rewrite our approach for machine translation completely. This paper discusses the process and the methodology to build the Machine translation system using Deep Neural Network (DNN) for translation between different Indian languages, using the combination of two ideas, i.e., Recurrent Neural Networks and Encoding.

In the 21st century, natural language processing (NLP) has obtained much prominence for human–machine interaction (HMI). With this interest in natural language processing (NLP) has grown significantly, numerous NLP tools (e.g., morphology, the tagger, and a parser, etc.) have been developed all over the world. Despite having huge importance and requirements, we have noticed gaps for having a comprehensive single framework or platform, which encompass all NLP-related tools and technologies for promoting the research in NLP and sharing the knowledge and resources among NLP researchers required for understanding and building the solution for HMI. Our objective is to apply Software engineering in natural language processing with the concept of an object-oriented model by using a collection of reusable objects by defining the communication protocol, consisting of a set of rules that must be applied to exchange data between two NLP modules. We proposed state of art ivrE—A virtual environment for creating, modifying, executing, and analyzing various NLP solutions and technology. The proposed idea is broadly based on to define own ivrE-NLP object framework model that permits the developer to create, modify, and execute the application and analyze their outcomes by operations on visual representations of the modules. A variety of NLP-based applications (tools, modules, and plugins) already exist, they can publish into store available with environment so it can be used by research community at large. To develop complete NLP framework or platform, we require much more than just assembling or collecting these tools or modules at one place, no matter how good any tool or module is working individually. It requires not only the standards and a set of protocols, but also requires a compliant composition than a pre-defined algorithms and their implementation. In brief, we require a comprehensive open framework to bundle, manage, and integrate set of NLP tools, modules, components, applications, algorithms, and define their associated rules, comprehensive data structures, and knowledge.

Deep neural network architectures are highly in-corporated in modern (progressive) automatic speech recognition(ASR) systems which outperforms conventional and hybrid systems. Even after adapting to such system we still facing a problem to make balance between accuracy and model size (no. of trainable parameters). We present our experiment in building a robust encoder-decoder based end-to-end ASR model for the Hindi language. Starting with, we have trained the QuartzNet model with two different architectures QuartzNet-5x5 and QuartzNet-15x5 by keeping the same training parameters and dataset. The speech corpus consists of 2648.9 hours of Hindi labelled audio data collected from various domains and sources. We have analyzed the performance of both models and observed that QuartzNet-15x5 has a radical improvement of 24% in accuracy. For building and training the models, we have used the PARAM SIDDHI AI system and training recipes from the open source NeMo toolkit.

With the rapid progress in the technology and data in the public domain, the machine translation and data science have made remarkable progress. In this paper, we discuss our specific use case of developing machine translation system for English to Hindi and Hindi to English language translation. For this system, we have used the daily proceedings of the Lok Sabha as data and developed NMT-based machine translation system on the top of already available rule-based machine translation system. Developed system has been evaluated using bilingual evaluation understudy (BLEU) as well as the human evaluation metrics using comprehensibility and fluency. In machine translation (MT), there is the trend of measuring post-editing time, and thus, we have also evaluated our system by measuring post-editing time using open-source tool.

In the modern era of industrialization, for every single second, enormous huge amount of data /corpus is generated. Not all the data generated is useful. As we see, when we crawl data from various websites or social media or newspaper, we get the lot of repeated type of data and finally we end up with lot of repeated data and store in a database as the resultant size of data goes on increasing and increasing. To avoid or overcome such type of problems, we are going to explain how to solve such problems by using a fuzzy match algorithms to predict whether this data is already available or not in a files or in the storage. In a nut shell, this paper is going to explain in details how to use the comparison between the different fuzzy algorithm in data matching so that it will help us in reducing the data storage and minimize our time in downloading the unique data. The fuzzy applies when we do not find exact match which is not a 100% match but relatable. This feature helps to give suggestions in search engines, in spell checker and many more. As we know by the year 2025 by CAGR prediction, the market size of data going to increase more than 30 Billion and this data is raw data which need to be aligned for other NLP or AI process which can be used in business understanding, data understanding and modelling, data preparation, etc. The Fuzzy matching approach, also called the probabilistic record linkage, is able to calculate the probability of various records which somehow means same or near around for the input string provided by user which is returned from the translation memory of data storage or a file. Our paper discusses this proposed model which applies the concept of fuzzy matching to make searching of strings easier in English language; the same approach can extended for all European and Indian languages.

This paper proposes language independent natural language generator for Tree Adjoining Grammar (TAG) based Machine Translation System. In this model, the TAG based parsing and generation approach considered for the syntactic and semantic analysis of a source language. This model provides an efficient and a systematic way of encapsulating language resources with engineering solution to develop the machine translation System. A TAG based Generator is developed with existing resources using TAG formalism to generate the target language from TAG based parser derivation. The process allows syntactic feature-marking, the Subject-Predicate Agreement marking and multiple synthesized generated outputs in complex and morphological rich language. The challenge in applying such approach is to handle the linguistically diversified features. It is achieved using rule-based translation grammar model to align the source language to corresponding target languages. Nevertheless, this paper also describes the process of lexicalization and explain the state charts, TAG based adjunction and substitution function and the complexity and challenges beneath parsing-generation process.

In this paper we describe an Intelligent System for Automatic Transfer Grammar creation using parallel corpus of source and target language. As we know about English and Hindi language, the structure of Hindi is Subject-Object-Verb (SOV) while in English the structure is Subject-Verb-Object (SVO). Now the system has to decide in which order to translate the given source language (English) to the given target language (Hindi). The grammatically parsing source sentence has to generate target language on basis of grammar rule which we have created based on parallel corpus. These unique rules are always applicable when the same input structure is found and regenerate the output on basis of the grammatical rules. Thus it gives better accuracy in terms of quality of translation. Reordering [1, 2] is important part of Transfer Grammar which is very helpful in language pairs of distant origin. We focus on designing a system that gives correct reordering for English-Hindi Machine Translation System including simple, compound as well as complex sentences. Reorder sentences gets generated based on probability. The system is evaluated on the basis of precision, recall and f-measure.

This Paper reveals the information about Deep Neural Network (DNN) and concept of deep learning in field of natural language processing i.e. machine translation. Now day's DNN is playing major role in machine leaning technics. Recursive recurrent neural network (R2NN) is a best technic for machine learning. It is the combination of recurrent neural network and recursive neural network (such as Recursive auto encoder). This paper presents how to train the recurrent neural network for reordering for source to target language by using Semi-supervised learning methods. Word2vec tool is required to generate word vectors of source language and Auto encoder helps us in reconstruction of the vectors for target language in tree structure. Results of word2vec play an important role in word alignment of the input vectors. RNN structure is very complicated and to train the large data file on word2vec is also a time- consuming task. Hence, a powerful hardware support (GPU) is required. GPU improves the system performance by decreasing training time period.

English is a language that is spoken by around 380-420 million people on this planet and understanding it is not at all easy. The meaning of a sentence varies according to the context and the tone of the speaker.To convey the thoughts efficiently, the knowledge of the language and its various rules is very important as thoughts take the form of words and the words take the form of action. One should aim to minimize the errors while using the language. Lesser is the number of mistakes, better will be the communication. To aid in achieving this goal, we are creating a frequency based spell checker and a rule based grammar checker for English language.The grammar checker focuses on detecting and correcting tense related mistakes.