Paper which does not follow the Double-blind, Springer template, Page limit will be desk rejected right away

Contents

CALL FOR PAPERS


This conference aims at bringing together researchers from across the world working on low-resourced and minority languages to create more speech and language technology for languages of the world.


We invite submissions on topics that include, but are not limited to, the following:

📚

Track-1: Language Resources (LRs)

  • Lexicons and machine-readable dictionaries
  • Linguistic Theories, Phonology, Morphological analysis, Syntax and Semantics
  • Corpus development, tools, analysis and evaluation
  • Issues in the design, construction and use of LRs: text, speech, sign, gesture, image, in single or multimodal/multimedia data
  • Exploitation of LRs in systems and applications
  • Annotation, analysis, enrichment of text archives
💻

Track-2: Language Technologies (LT)

  • Code-mixing
  • Cognitive modeling and psycholinguistics
  • Computer-assisted language learning (call)
  • Covid-19 alert, NLP applications for emergency situations and crisis management
  • Equality, diversity, and inclusion for language technology
  • Fake news, spam, and rumour detection
  • Hate speech detection and offensive language detection
  • Machine translation, sentiment analysis, and text summarization
  • Text and data mining for social sciences and humanities research
  • Text and data mining of (bio) medical literature, including pandemics
  • Knowledge representation and reasoning
  • Knowledge graphs for corpora processing and analysis
  • Applications for language, data and knowledge
  • Question answering and semantic search
  • Text analytics on big data
  • Semantic content management
  • Computer-aided language learning
  • Natural language interfaces to big data
  • Knowledge-based NLP
🎤

Track-3: Speech Technologies (ST)

  • Speech technology and automatic speech recognition
  • Spoken dialog systems and analysis of conversation
  • Spoken language processing — translation, information retrieval, summarization resources and evaluation
  • Speaker verification and identification
  • Multimodal/multimedia speaker recognition and diarization
  • Analysis of speech and audio signals
  • Speech coding and enhancement
  • Speech recognition - architecture, search, and linguistic components
  • Speech, voice, and hearing disorders
  • Speech synthesis and spoken language generation
  • Cross-lingual and multilingual components for speech recognition / code switching
👁️

Track-4: Computer Vision and Natural Language Processing (NLP)

  • Image Captioning
  • Optical Character Recognition
  • Handwritten Recognition
  • Visual Question Answering
  • Machine learning for multimodal interaction
  • Mobile multimodal systems
  • Multimodal behaviour generation
  • Multimodal datasets and validation
  • Multimodal dialogue modeling
  • Multimodal fusion and representation
  • Multimodal interactive applications
  • Novel multimodal datasets
🚀

Track-5: Applications of NLP

  • NLP for Social media applications
  • Federated Learning
  • Disordered Speech with NLP
  • Explainable models for speech and NLP technologies
  • Quantum Computing with NLP
  • Conversational agents using NLP and Speech technologies
  • Cross cultural NLP
  • NLP in Education
  • Leveraging NLP and Speech technologies to promote heritage and culture
⚖️

Track-6: Federated Learning & Ethical NLP

  • Ethics, Bias, and Legislation in Speech, Vision, and NLP
  • Digital privacy and identity management in NLP and Speech Technologies
  • Explainability of NLP and speech technology tools
  • Bias in Large Language Models (LLMs) and multimodal model
  • Bias in security related NLP and Speech datasets and annotations
🧠

Track-7: Knowledge Graphs, Large Language Models, and Multimodal AI for Low-Resource Languages

  • Augmenting LLMs with knowledge graphs
  • Fine-Tuning LLMs with knowledge graphs
  • Using knowledge graphs as plugins
  • Knowledge graph construction assisted by LLMs
  • Ontology engineering
  • Cross-lingual knowledge alignment and language-agnostic representation of knowledge
  • Automated knowledge graph construction from low-resource language corpora
  • LLM-assisted entity and relation extraction for indigenous and low-resource languages
  • Cross-lingual knowledge graph creation using multilingual LLMs
  • Knowledge graph population from noisy and sparse data sources
  • Domain-specific knowledge graphs for low-resource languages
  • Multimodal knowledge graph construction from text, speech, images, and video
  • Vision-language models for knowledge extraction in low-resource settings
  • Speech-to-knowledge graph pipelines for underrepresented languages
  • Grounding multimodal knowledge in indigenous and regional languages
  • Multimodal entity linking and disambiguation
  • Retrieval-Augmented Generation (RAG) using knowledge graphs for low-resource languages
  • LLM-based knowledge graph completion and refinement
  • Neuro-symbolic approaches combining LLMs and knowledge graphs
  • Hallucination detection and mitigation using knowledge graphs
  • Cross-lingual knowledge transfer for low-resource languages
  • Multilingual and code-mixed knowledge graphs
  • Aligning low-resource language knowledge graphs with global knowledge bases
  • Knowledge graph embeddings for multilingual applications
  • Knowledge representation for linguistically diverse communities
  • Indigenous knowledge graphs and cultural heritage preservation
  • Knowledge graphs for oral traditions and folklore
  • Community-centric knowledge acquisition and validation
  • Representation of cultural semantics in low-resource languages
  • Ethical knowledge graph construction for indigenous communities
  • Knowledge graphs for education and digital inclusion
  • Healthcare and agriculture knowledge graphs for local languages
  • Fact checking and misinformation detection using knowledge graphs
  • Benchmark datasets for knowledge graph research in low-resource languages
  • Evaluation metrics for multimodal knowledge graph construction
  • Human-in-the-loop knowledge graph curation
  • Explainability and trustworthiness in knowledge graph-enhanced LLMs
  • FAIR and open knowledge resources for low-resource languages
  • Graph foundation models for low-resource languages
  • Agentic AI for autonomous knowledge graph construction
  • Temporal and dynamic knowledge graphs from multimodal streams
  • Federated and privacy-preserving knowledge graph learning
  • Knowledge graphs for multimodal reasoning and explainable AI

AI-ASSISTED RESEARCH DISCLOSURE GUIDELINES


The SPELLL 2026 conference has embraced the Springer Nature Policy regarding AI writing tools, which mandates authors to disclose AI assistance in their research. Springer Nature is monitoring ongoing developments in this area closely and will review (and update) these policies as appropriate.

  1. AI authorship.
  2. Generative AI images.
  3. AI use by peer reviewers.

For more details please visit: https://www.springer.com/gp/editorial-policies/artificial-intelligence--ai-/25428500?srsltid=AfmBOoreWb8ZbuJy6R4k3sh4EFD5mXZ1-g5W7mJn3Siq_4qVKxBC-bZH

In all cases, authors are responsible for the correctness of their methods, results, and writing. Authors should check for potential plagiarism, both of text and code.

SUBMISSION GUIDELINES

Regular Papers

Regular submissions must describe substantial, original, completed and unpublished work. Wherever appropriate, concrete evaluation and analysis should be included.

Regular papers may consist of 12 - 16 pages of content including references.

Short Papers

SPELLL 2026 also solicits short papers. Short paper submissions must describe original and unpublished work. Short papers should have a point that can be made in a few pages. Some kinds of short papers are:

  • A small, focused contribution
  • Work in progress
  • Experience notes

Short papers may consist of 6 - 8 pages including references. Short papers will be presented in one or more oral or poster sessions. While short papers will be distinguished from regular papers in the proceedings, there will be no distinction in the proceedings between short papers presented orally and as posters.

Review Policy

For SPELLL 2026, the evaluation of submissions will employ a double-blind review process, ensuring impartiality and confidentiality in the assessment of papers. Under this system, the identities of both the reviewers and the authors are kept anonymous. This means that authors do not know who reviews their papers, and reviewers are unaware of the authors' identities. This approach is designed to minimize biases related to the authors' background, affiliation, or previous work, promoting an objective evaluation based on the submission's originality, relevance, importance, and clarity. Furthermore, authors are required to maintain anonymity in their citations as well. When referring to their previous work, authors should use the third person to avoid revealing their identity. For example, instead of saying "In our earlier work..." or "We previously showed that...", authors should frame these citations as if referencing another researcher's work, such as "Smith et al. (2020) demonstrated that...". This guideline helps preserve the integrity of the double-blind review process, ensuring that papers are evaluated solely on their merits.

Notice

  • Paper which does not follow the Double-blind, Springer template, Page limit will be desk rejected right away

Author Guidelines

  • When submitting a paper, authors must rigorously follow the double-blind policy guidelines. This includes omitting names and affiliations from the submission and ensuring adherence to the review policy guidelines to maintain anonymity.
  • Authors must follow the Springer CCIS formatting instructions.
  • For camera-ready papers use Latex or Word style provided on the authors' page for the preparation of papers.
  • The LaTeX Proceedings Template for scientific authoring platform in Overleaf.
  • Latex Template: https://resource-cms.springernature.com/springer-cms/rest/v1/content/19238648/data/v8 (Preferable)

    (OR)

    Word Template: https://resource-cms.springernature.com/springer-cms/rest/v1/content/19238706/data/v5
  • For Latex version – upload into Overleaf online latex editor or local Latex editors to prepare the paper.

    Overleaf link: https://www.overleaf.com/

  • Follow double-blind policy guidelines – It means, don’t add author’s name(s), affiliations, email id, etc. in the paper.
  • Paper should follow the page limit constraints as mentioned above.
  • Paper should contain only Title, Abstract, Keywords and Necessary Subsections in the above Springer CCIS format. This copy you have to upload in the following submission link in PDF format.
  • Each paper will receive at least three reviews. At least one author of each accepted paper must register by the early registration date indicated on the conference website and present the paper.

Submission Link: https://openreview.net/group?id=SPELLL.org/2026/Conference#tab-your-consoles

For LTP : SNCS_ProceedingsPaper_LTP_ST_SN_Switzerland.docx

PUBLICATION

Accepted papers that are presented at the conference will be published in the Springer series: Communications in Computer and Information Science (CCIS).

Volumes published will be indexed in DBLP, Google Scholar, EI-Compendex, Mathematical Reviews, SCImago, Scopus. CCIS volumes are also submitted for the inclusion in ISI Proceedings.