Why Sri Lankan Tamil Speech Synthesis Requires Target-Specific Data and Models Text-to-Speech (TTS) has advanced rapidly from conventional statistical parametric synthesis to neural architectures capable of generating highly natural, expressive and speaker-controlled speech. Modern systems such as VITS, VITS2, YourTTS, StyleTTS2, F5-TTS, IndicF5 and newer multilingual speech models have demonstrated impressive capabilities in multilingual synthesis, ...

A Low-Resource ASR Adaptation Study at CTNLPR Center for Tamil Natural Language Processing Research (CTNLPR) Introduction Automatic Speech Recognition (ASR) has undergone a fundamental transition from language-specific acoustic models toward large-scale multilingual speech foundation models. For Indic languages, this transition has been particularly significant. Systems such as IndicWav2Vec, IndicWhisper, IndicConformer, and newer multilingual speech models ...

Why building a Knowledge Graph is far more than extracting entities and relationships. Introduction Knowledge Graphs have become one of the fundamental building blocks of modern Artificial Intelligence. From semantic search engines and digital libraries to Retrieval-Augmented Generation (RAG), question answering, recommendation systems, and scientific knowledge discovery, many intelligent applications rely on structured knowledge rather ...

The rapid advancement of Transformer-based multilingual language models has significantly improved Named Entity Recognition (NER) across many languages. Models such as IndicBERT, MuRIL, XLM-R, mBERT, and more recently Large Language Models (LLMs), have substantially advanced multilingual NLP by learning contextual representations from massive corpora. Despite these advances, Tamil continues to present unique challenges for Named ...

Introduction Named Entity Recognition (NER) is one of the fundamental sequence labeling tasks in Natural Language Processing (NLP), where each token in a sentence is assigned a semantic label representing real-world entities such as persons, organizations, locations, products, events, and creative works. NER serves as a foundational component for numerous downstream AI systems, including: Information ...

Introduction Knowledge Graph construction from unstructured text remains one of the fundamental challenges in Natural Language Processing (NLP). The process requires identifying entities, understanding semantic relationships between them, and converting textual information into structured triples that can be stored, queried, and reasoned over. For low-resource languages such as Sri Lankan Tamil, this challenge is even ...

Modern Information Extraction systems rely on more than Named Entity Recognition (NER). While NER can identify entities such as people, locations, and organizations, it does not explain how references to those entities evolve throughout a document. This is where Coreference Resolution (CR) becomes essential. Coreference Resolution is the task of determining when multiple mentions within ...

The rapid evolution of transformer-based multilingual NLP systems has significantly improved Named Entity Recognition (NER) performance across many high-resource languages. However, low-resource language variants such as Sri Lankan Tamil still face substantial challenges due to limited domain-specific datasets and linguistic underrepresentation in existing multilingual training corpora. At CTNLPR (Center for Tamil Natural Language Processing Research), ...

The growth of Large Language Models (LLMs) and multilingual NLP systems has significantly improved language technologies across major global languages. However, low-resource languages such as Sri Lankan Tamil still face a severe lack of high-quality annotated datasets—especially for foundational tasks like Named Entity Recognition (NER). To address this gap, we developed the Srilankan-Tamil-NER Dataset, a ...

Coreference Resolution (CR) is one of the most important tasks in Natural Language Processing (NLP). It focuses on identifying whether multiple expressions within a document refer to the same real-world entity. Resolving these semantic references is critical for document-level understanding and directly impacts downstream NLP systems such as: Information Extraction Knowledge Graph Construction Question Answering ...