Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65504
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of Computer Science
JSPM University,Wagholi, Pune
Department of Computer Science
JSPM University,Wagholi, Pune
This paper presents the design and development of a real-time AI-based voice agent system that can handle live voice conversations accurately and with minimal delay. The system uses a browser-based interface for easy user interaction and FastAPI as the backend framework to manage communication and processing tasks. Real-time audio streaming and processing are managed through a dedicated agent built on LiveKit. The system integrates the Google Gemini Realtime API for speech recognition and intelligent response generation. It also supports continuous streaming of logs, conversation transcripts, and system updates using Server-Sent Events (SSE). The architecture is designed to be modular, scalable, and efficient, making it suitable for applications such as AI assistants, customer support services, and other voice-based interactive systems.
KEYWORDS
The proposed real-time AI voice assistant successfully combines speech recognition, intelligent response generation, and noise reduction into a single system. It overcomes many limitations of traditional voice-based systems by providing fast, reliable, and real-time communication. By using technologies such as FastAPI, LiveKit, Silero VAD, and the Google Gemini API, the system is able to process voice input accurately and generate responses with minimal delay. This makes it suitable for various applications, including virtual assistants, customer support services, and interactive voice-based platforms. The system performs effectively even in noisy environments, improving the overall user experience. Its scalable and flexible design also provides a strong foundation for future improvements and the addition of more advanced features.
[1] X. Yu, Y. Ju, X. Yuan, L. Yang and H. Zhang, "A Multi-Speaker Speech Processing System for Dialect- Rich Environments with LLM-Based Optimization," 2025 9th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC), Beijing, China, 2025, pp. 213-217, doi: 10.1109/IC- NIDC67200.2025.11390222. [2] H. Jafarzadeh Asl, M. Ghazvini Nejad, A. Edraki, M. Asgharian and V. Partovi Nia, "Tiny Noise-Robust Voice Activity Detector for Voice Assistants," 2025 IEEE 35th International Workshop on Machine Learning for Signal Processing (MLSP), Istanbul, Turkiye, 2025, pp. 1-6, doi:10.1109/MLSP62443.2025.11204268. keywords: {Voice activity detection;Accuracy;Personal voice assistants;Transforms;Noise robustness;Background noise;Human voice;Noise measurement;Internet of Things;Smart glasses;Noise Robustness;SpeechRecognition;Voice activity Detection},I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350. [3] M. Yamano, Z. Song and J. Hoshino, "Augmenting the Social Presence of Interactive Characters Using Real- time Speech Recognition," 2022 Nicograph International (NicoInt), Tokyo, Japan, 2022, pp. 85-88, doi: 10.1109/NicoInt55861.2022.00024. [4] K. N., R. V., S. S. S. and D. R., "Intelligent Personal Assistant - Implementing Voice Commands enabling Speech Recognition," 2020 International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India, 2020, pp. 1-5, doi: 10.1109/ICSCAN49426.2020.9262279. [5] R. Manimegalai, M. Shobana, C. Rajesh, C. Parthasarathy, A. V. Sriharsha and T. D. Kumar, "Adaptive Emotion-Aware Speech Synthesis on Edge Devices: A Lightweight Dynamic Model Adaptation Framework using LoRA and OnDevice Fine-Tuning," 2026 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India, 2026, pp. 1513-1520, doi: 10.1109/ICEARS67481.2026.11416528. keywords: {Training;Adaptation models;Renewable energy sources;Quantization (signal);Speech enhancement;Real-time systems;Physiology;Speech synthesis;Reliability;Robots;Speech Synthesis;Edge Devices;Low-Rank Adaptation;Lightweight Neural Adapters;On-Device Learning}
Publication Date
6/25/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
4
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...