Engineering and Technology
| Open Access |
DOI: Adaptive Voice Intelligence Platform: A Five-Layer Architecture for Self-Learning, Context-Aware Commercial Interactions
Vivek Sharma , Independent AI Researcher, USAAbstract
This article presents a novel five-layer adaptive voice intelligence platform that transcends the limitations of traditional command-based voice interfaces by implementing a comprehensive architecture for self-learning, context-aware commercial interactions. The proposed system addresses critical gaps in current voice technology through the integration of [Large Language Models] LLM-augmented multi-turn intent parsing, contextual session graph engines, zero-shot voice workflow compilation, reinforcement-tuned optimization, and comprehensive evaluation frameworks. Unlike existing voice assistants that rely on predefined commands and static decision trees, this platform enables natural conversational interactions capable of understanding complex, multi-constraint queries while maintaining persistent memory across sessions and devices. The architecture demonstrates significant improvements in intent recognition accuracy, task completion rates, and user satisfaction across diverse industry deployments, including retail, financial services, healthcare, logistics, and accessibility applications. Through its no-code configuration capabilities and continuous learning mechanisms, the platform democratizes voice interface development while ensuring enterprise-grade security, explainability, and regulatory compliance. This article establishes a transformative framework that elevates voice from a supplementary input method to a primary interface modality, providing a foundation for realizing truly intelligent human-computer interaction that matches and potentially exceeds traditional graphical user interfaces in efficiency, accessibility, and user engagement.
Keywords
Voice intelligence platform, Adaptive conversational AI, Context-aware commerce, Self-learning voice systems, Multimodal human-computer interaction
References
Bret Kinsella et al., "Voice Assistant Consumer Adoption Report 2022: Smart Speaker and Voice AI Usage Patterns," Voicebot Research, 2022. [Online]. Available: https://voicebot.ai/2022/04/15/voice-assistant-adoption-clustering-around-50-of-the-population/
Matthew B. Hoy, "Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants," Medical Reference Services Quarterly, vol. 37, no. 1, pp. 81-88, 2018. [Online]. Available: https://doi.org/10.1080/02763869.2018.1404391
T. Brown et al., "Language Models are Few-Shot Learners," in Advances in Neural Information Processing Systems, vol. 33, pp. 1877-1901, 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
A. Vaswani et al., "Attention is All You Need," in Advances in Neural Information Processing Systems, vol. 30, pp. 5998-6008, 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
J. Devlin, M. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pp. 4171-4186, 2019. [Online]. Available: https://aclanthology.org/N19-1423.pdf
V Mnih et al., "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, pp. 529-533, 2015. [Online]. Available: https://www.nature.com/articles/nature14236
Karen Scates, "Four Keys to Implementing a Voice Shopping Experience," SoundHound Inc., 2022. [Online]. Available: https://www.soundhound.com/voice-ai-blog/four-keys-to-implementing-a-voice-shopping-experience/
NVIDIA Corporation, "Build Conversational AI Solutions," NVIDIA AI Solutions.[Online]. Available: https://www.nvidia.com/en-in/solutions/ai/conversational-ai/
Geoffrey Hinton et al., "Deep Neural Networks for Acoustic Modeling in Speech Recognition," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012. [Online]. Available: https://ieeexplore.ieee.org/document/6296526
NVIDIA, "Deep learning," [Online]. Available: https://developer.nvidia.com/deep-learning
Samantapudi, R. K. R. (2025). Advantages & impact of fine tuning large language models for ecommerce search. Journal of Information Systems Engineering and Management, 10(45s), 600–622. https://doi.org/10.52783/jisem.v10i45s.8898
Venkiteela, P. (2025). Machine Learning Framework for Retail Sales Forecasting. International Journal of Computational and Experimental Science and Engineering, 11(4). https://doi.org/10.22399/ijcesen.3993
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Copyright (c) 2025 Vivek Sharma
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