AI-Powered Tools to Improve Tamil Language Proficiency

Introduction

The fractured landscape of multimodal AI is a barrier to professional-level storytelling for non-western cultures. Educators are using multiple and individual services ChatGPT, DALL•E, Eleven Labs, Runway ML, CapCut, Unity 3D - partially completing their workflows, paying multiple subscription fees, and moving data manually. As a result, large scale cultural projects are financially unaffordable, slow and persevering.

1. Principal Ideas:

• Fragmentation Problem: The main problem is that producing comprehensive multimedia content currently requires a number of different, expensive, and disjointed Artificial Intelligence tools. This limits scalability, creates fragmentation in the creative process, incurs greater costs, and requires time switching between tools.

1. Cultural Preservation through AI: The primary aim is to use AI to develop immersive multimedia and augmented reality experiences to make classical Tamil texts - as the example Purananuru - accessible and engaging to today's audiences.

2. The development of a single, integrated AI platform that covers all essential creative workflows, from text interpretation to AR transformation, is the main solution put forth.

3. Democratization of AI Storytelling: By streamlining processes and cutting expenses, the platform hopes to enable a larger group of users—including researchers, educators

4. Democratization of AI Storytelling: By streamlining processes and cutting expenses, the platform hopes to enable a larger group of users—including researchers, educators, and students—to produce excellent cultural content without facing major financial or technical obstacles.

Key Concepts and Information:

Fragmented AI Workflow for Cultural Storytelling: The Issue

• Present Situation: For various phases of content production, creators presently employ "separate tools like ChatGPT, DALL·E, RunwayML, ElevenLabs, CapCut, and more."

5. The "fragmented landscape creates barriers to professional storytelling and cultural engagement," according to the identified barriers.

6. Consequences in particular: Expensive: Users must utilize "multiple paid platforms."

7. Tool switching takes a lot of time because outputs from various tools must be manually integrated.

8. Lack of a smooth transition between stages is a sign of a fragmented creative process.

9. Restricted scalability: Implementing "large-scale cultural digitization and classroom use" is challenging.

A particular consequence of this fragmentation is that it "hinders the democratization of AI-powered storytelling, particularly in non-Western languages like Tamil."

Literature Survey

Evangeline and Moorthy (2023) introduced a bilingual next-word prediction framework for Tamil AAC systems by utilizing Tamil-English translation to make use of existing large datasets, including past occurrences of English [1]. The authors explored various deep learning solutions: "Looking at LSTM, BiLSTM, GRU and BERT models and tested varying sentence lengths to predict" (Evangeline & Moorthy, 2023). In discussing their findings, they highlighted certain successes, stating, "The GRU models produced the highest precision of .75 and BLEU score" (Evangeline & Moorthy, 2023). They concluded that GRU was the most suitable model for their Tamil AAC project [1]. Interestingly, Evangeline and Moorthy (2023) acknowledged "the limitations their approach encountered due to semantic loss that occurred due to translation", which is a frequently noted limitation in cross-lingual methods. The authors suggested that "the application of the framework to other low-resourced languages will need to be further validated using state-of-the-art models including GPT" (Evangeline & Moorthy, 2023), thus sparking further research ideas [1].

Raju (2023) studied possible approaches to integrating AI into English Language Teaching (ELT). The study was conducted in India which has unique cultural and socio-economic factors [2]. The author categorized AI applications accordingly, stating: "AI potential can be categorized into speech recognition, natural language processing, intelligent tutoring systems, and adaptive learning platforms" (Raju, 2023). However, Raju (2023) highlighted challenges to implementation by stating: "The main challenges associated with Transformer AI were context-based challenges that were primarily resource-driven, levels of access determined by wealth, wide variations of dialect and language that exist in India" [2]. This quote reveals the level of complexity in regards to AI deployment in contexts where institutions are quite different. While they tended to focus on theoretical implications, Raju (2023) also recognized that "the research has mostly not addressed practical implications to the Indian ELT context", and concluded there were no "robust measures of effectiveness for effectively integrating AI into the complicated educational landscape in India" (Raju, 2023), revealing the lack of theoretical or practical implementation studies [2]

Banumathi et al. (2023) provided an example of an AI-enabled Tamil language mobile app designed for early dyslexia screening for young children, specifically those aged 5-8 years [3]. The researchers underscored the importance of their research by noting that "15% of children suffer from dyslexia" (Banumathi et al., 2023), demonstrating the important need for early screening tools in educational settings. The app offered comprehensive assessment abilities, and the authors noted that "the app examined eight areas of assessment: novelty, general characteristics, health, speech and hearing skills, visual performance, reading and writing skills, maths skills, memory and cognition skills" (Banumathi et al., 2023), while machine learning algorithms provided estimates of classification accuracy [3]. However, the researchers noted that there were significant limitations to this research, indicating that "the validation of the app was bound to only specific age ranges" and there was "limited evidence for generalising across varying socio-economic backgrounds and dialects of Tamil" (Banumathi et al., 2023), which indicates important gaps for future research [3].

Angelin Jeba et al. (2023) developed a complete Tamil transcript summarization tool for YouTube video material [4]. The authors described their work as "an integrated natural language processing, translation to speech product which was able to generate contextual understanding of the video transcript" (Angelin Jeba et al., 2023), using the YouTube Transcript API and spaCy for Python-based extractive summarization. In addition to the quality of the transcripts, there is the issue of the outputs provided as they had the flexibility to allow users select how summaries could be outputted and the authors commented that participants would have their summaries "classified and categorized into user defined summary length (i.e. small, medium, large) including translating summaries from English into Tamil using Google text-to-speech (gTTS) " (Angelin Jeba et al., 2023) [4]. Based on the authors description of the software application's interface 

it was user friendly as it was based on Tkinter for python and had user definable start parameters. Irrespective of the user friendliness with respect to the input dataset and outputs, the authors do acknowledge there was considerable challenges to address as they suggest, "the system did encounter major challenges with respect to Tamil, as the language itself has complex grammar, a considerable number of dialects and colloquial phrases" (Angelin Jeba et al., 2023). Moreover, they also highlight the weaknesses with, 'the method of extractive summarization may not account for some aspects of contextual understanding' and last, but not least, they understanding' and last, but not least, they acknowledge that 'the problems relating to extracting transcripts containing poorly transcribed Tamil cannot be overlooked, especially as a function of the quality of the transcript provided by YouTube' (Angelin Jeba et al., 2023) thus raising both technical limitations and data

The novel Multi-Stage Deep Learning Architecture (MSDLA) for cross-lingual sentiment analysis of Tamil language has been proposed by Jothi Prakash and Arul Antran Vijay (2023), presumably to better handle the challenges of low-resource environments for the target language by utilizing transfer learning from resource-rich source languages in some manner. Their proposed MSDLA architecture demonstrated the potential to improve performance significantly. The researchers noted, "achieving improved performance on the Tamil Movie Review dataset measuring accuracy, precision, recall, and F1-scores of 0.8772, 0.8614, 0.8825, and 0.8718, respectively." (Jothi Prakash & Arul Antran Vijay, 2023). Further improvements were noted the authors stated: "the MSDLA architecture outpredicted existing architectures like mT5, XLM, mBERT, ULMFiT, BiLSTM LSTM with Attention, and ALBERT with p-values less than 0.005" (Jothi Prakash & Arul Antran Vijay, 2023) [5]. Nonetheless, the research authors noted some limitations. Specifically, "the MSDLA architecture's complexity via multi-stage processing could potentially increase computational overhead and training time" (Jothi Prakash & Arul Antran Vijay, 2023). The researchers also found that "the removal of cross-lingual semantic attention and domain adaptation demonstrated drops in performance to accuracies of 0.8342 and 0.8043," (Jothi Prakash & Arul Antran Vijay, 2023), which indicates some brittleness in the interactions of the MSDLA component architectures, as well as, limited possibilities for scalability to other low-resource language applications without exhaustive fine tuning (3).

Khan and others (2023) gave an extensive survey of the advancements of deep learning and natural language processing research across multiple architectures and implementations [6]. The authors took a methodical review of multiple technologies and stated that "our review examined neural networks, CNNs, and deep belief networks on a number of tasks, including machine translation, speech processing or text classification" (Khan et al., 2023). The reviewed articles took a comprehensive approach in that they included "a review of DL applications in six pattern recognition tasks and analyzed the nature of embeddings from a taxonomic perspective" (Khan et al., 2023) [6]. Although Khan et al. (2023) indicated significant drawbacks with methods recruited in this line of inquiry, specifically that "large models faced significant challenges with pragmatic language aspects because of foibles in statistical learning and terms like contextual understanding" (Khan et al., 2023). Additionally, Khan et al. (2023) acknowledged methodological constraints in hopes of illuminating the gap in the research, as "the review encompassed exposed limited specificity around low-resource languages and verified them from an empirical perspective across language variation", illustrating their coverage gaps around under-represented languages, such as Tamil [6].

Proposed Methodology: 

Generating End-to-End AR Storytelling Experiences

In order to create end-to-end AR storytelling experiences from classical Tamil literature, the main objective is "To develop a conceptual framework for a unified AI platform."Simplifying content creation, cutting expenses, and promoting cultural preservation are the goals of this.

Limitations of the Current Workflow (Step-by-Step Analysis):

The current workflow is very expensive and fragmented: The current process for turning a Purananuru verse into an augmented reality experience is typified by its manual integration at every step and dependence on multiple, unrelated AI tools.The statement "Each module requires separate subscriptions and manual integration" is true. Particular Tools Needed (and whether they are paid for):

•  Selection and analysis of verses: ChatGPT, Claude, Gemini.-paid

10.  Quick generation: paid ChatGPT and Vision

11. Visual generation: runwayML, DALL·E, Sora, Midjourney, and Pika Labs (paid)

12. Writing dialogue: ChatGPT (Poetic tone in Tamil) - (paid)

13. Google TTS, iSpeech, ElevenLabs, and manual, paid methods for synthesizing Tamil voices -(paid ) 

14. Subtitle alignment: CapCut / Descript / ChatGPT timestamp -paid 

15. AR conversion: paid Unity 3D + ARKit + manual upload    -paid. Video assembly: DaVinci Resolve, Premiere Rush, and CapCut -paid

Implications: The requirement for multiple tools leads to a laborious, time-consuming process ("manual integration") and a significant financial investment (due to "separate subscriptions").

The Proposed Solution is an Integrated Multimodal AI Platform. The primary goal is to develop "a single AI interface" with fully integrated, functional modules. The goal of this platform is to combine all required procedures and resources into a single ecosystem.

"Building a single AI interface" with integrated functional modules is the central idea

Principal Benefits of the Suggested Platform: The unified platform addresses operational and financial inefficiencies and provides notable enhancements over the existing workflow.

It directly removes the need for "separate premium tool subscriptions." Is "money-saving"

"Time-efficient": Achieved by eliminating "tool-switching" and consolidating "all steps in one place."

"User-friendly": Designed to be accessible, allowing "Creators, educators, and researchers can work with minimal setup."

"Scalable": Positioned as "ideal for large-scale cultural digitization and classroom use," suggesting its potential for broader impact beyond individual projects

Features of the Suggested Unified Platform Modules: 

 A number of specialized modules that concentrate on distinct aspects of the process of producing augmented reality experiences are supposed to be included in the platform:

• Tamil Verse Analyzer: "Interprets classical texts with cultural nuance." This is crucial for accurately understanding the Purananuru verses.

16. Visual Prompt Composer: "Generates scene descriptions from poetic themes," acting as the bridge between text and visual generation.

17. Visual Synthesizer: "Produces stills or animated clips using embedded vision API." This is the core visual creation component.

18. Dialogue Generator: "Crafts poetic Tamil voiceover scripts," ensuring linguistic and stylistic accuracy.

19. Voice Synthesizer: "Speaks dialogues in native Tamil with emotion controls," adding a layer of realism and cultural authenticity.

20. Subtitle Renderer: "Aligns multilingual subtitles based on tone markers," facilitating accessibility.

21. AR Export Engine: "Converts output into an AR-ready format for mobile/web," the final step for AR deployment.

22. Story Editor Module: "Merge visuals, voice, and subtitles into a final video, with music and transitions" (from the second source), indicating a comprehensive post-production capability within the platform

Language-Specific Challenges and API Issues:

Creating an integrated AI platform to support storytelling in classical Tamil literature like Purananuru, obviously carries some challenges linguistically and technologically. Have we had issues executing it in Tamil?

Yes, when we used more mainstream APIs (like ChatGPT, DALL·E, and ElevenLabs) - the majority of these are optimised for English - the team experienced a substantial amount of limitations in trying to use these APIs for Tamil. Some examples include: 

• Poor modelling of complex Tamil grammar and poetic structure.

• Tamil voice outputs that were emotionally flat or unnatural, which are from generic TTS engines. 

• Visual generation was devoid of cultural context when prompted with Tamil descriptions. 

These barriers led to in losing some authenticity and also took away from the immersive quality we'd like to create when sharing heritage stories.

Have we considered APIs that are specific to Tamil?

Yes. This is a fundamental part of our roadmap. We will also explore integrations with Tamil-language AI APIs like those being offered by AI4Bharat. These are promising Tamil language resources and tools offer:

• Pre-trained Tamil language models (e.g. IndicBERT, MuRIL).

• Speech and text tools tailored for Indian languages.

• Emotion-rich Tamil TTS and emotion-aware processing.

• There is also a potential to create our own modules to facilitate:

• Meaningful interpretation and translation of poetic Tamil.

• Context-driven visual generation that carry cultural motifs.

• Tamil subtitle alignment that uses regional

• This initiative would "revolutionize digital humanities efforts across India and beyond."

Future Roadmap & Improvements:

• Build "open-source Tamil models with emotional tone via AI4Bharat collaboration."

• Train "historically accurate style datasets for visual generation."

• Launch "beta platform for educators and researchers."

• Develop "interactive features for real-time story selection and multilingual narration."

• Offer a "freemium model with open academic access."

• Address current challenges like "Emotionally flat Tamil TTS" and "Generic outputs lacking cultural motifs" by training "regional voice models via AI4Bharat" and using "curated datasets for Tamil aesthetics."

The future roadmap for the proposed unified AI platform focuses on continuous improvement and expanded accessibility.

The areas of development are:

• • Improved Language Models: The proposal is to create the open-source Tamil model with emotion tone through AI4Bharat to combat the limitations with what is currently "Emotionally Flat Tamil TTS" or default generated voice output that sounds robotic or unnatural and does not include 'emotional,' tone.

• • Culturally Accurate Visuals: We will then address "Generic outputs without cultural motifs" by training historically accurate style datasets to use for visual generation. The delivery of visuals will be culturally relevant, like for representing Sangam-era scenes, instead of using generic visuals.

• • Platform Rollout and Access: A beta platform will be launched to create access for educators/researchers for real-world use case testing and subsequently obtain feedback for the technical developments. If there is enough initial interest in the subject, a freemium model or open academic access (for full access) is also envisioned to increase public access to wider technology use and to democratize cultural preservation use.

• • Interactive Storytelling: Eventually, future enhancements will include interactive story features for real-time story selections from scene development and pluralistic storytelling (narration in multiple-language storylines and alternating styles), enabling more flexible user experiences.

• These steps collectively aim to refine the platform's capabilities, ensure cultural fidelity, and broaden its reach to a diverse user base, ultimately "revolutionizing digital humanities efforts".

• The future roadmap for the unified AI platform focuses on continuous enhancement and broader accessibility.

Planned enhancements include:

• • Developing open-source Tamil AI models that contain emotional tone with assistance from AI4Bharat in response to the current challenge of "Emotionally flat Tamil TTS".

• • Training visual generation models with Tamil style datasets that are historically accurate to resolve the issues of "Generically outputting and lacking cultural motifs" to further promote authenticity through cultural relevance.

• • Launching a beta platform for engaging educators and researchers to test and provide feedback..

• Create a beta platform enabling educators and researchers to engage in their own testing and feedback. 

• • Add interactive features enabling dynamic story selection and narration in multiple languages.

• • Intended implementation of a freemium model with open academic access to make freely usable…

Conclusion:

."The lack of a single platform for multimodal AI storytelling hampers professional creation and cultural digitization—especially for regional literature like Purananuru."

The proposed system will "unlock a world where everyone—from students to scholars—can create immersive heritage experiences, without technical roadblocks or financial barriers."

References:

1. 1. Evangeline, P., & Moorthy, S. (2024). A bilingual AI-powered   next-word prediction framework for Tamil AAC systems using translation-based resource leveraging. Journal of Augmentative and Alternative Communication Systems, 15(3), 45-62. 

2. 2.  Raju, K. (2024). Transformative potential of artificial intelligence in  English language teaching across India's diverse educational landscape. International Journal of Educational Technology, 28(4), 112-128. 

3. 3. Banumathi, R., Kumar, S., & Priya, M. (2024). AI powered Tamil smartphone application for early screening of dyslexia in children: A machine learning approach. IEEE Transactions on Biomedical Engineering, 71(8), 234-245. 

4. 4.Angelin Jeba,,M.,Sharma, P., & Krishnan, R. (2024). A comprehensive Tamil transcript summarization system integrated with NLP and text to speech functionality. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(2), 78-95. 

5. 5. Jothi Prakash,V., & AntranVijay, A. (2024). A multi-stage deep learning architecture for cross-lingual sentiment analysis of Tamil language using transfer learning. Neural Computing and Applications, 36(12), 6789-6804. 

6. 6. Khan, S., Ahmed, M., & Chen, L. (2024). Deep learning and natural language processing: A comprehensive survey of neural network and pattern recognition applications. IEEE Transactions on Neural Networks and Learning Systems, 35(6), 1456-1473.