Keyword Spotting System

Abstract

The project “KEYWORD SPOT” is a Machine Learning-based Web Application designed for Few-Shot Language Agnostic Keyword Spotting (FSLAKWS), enabling accurate detection of spoken keywords across multiple languages using minimal training data. By leveraging Few- Shot Learning with Prototypical Networks, the system achieves rapid keyword recognition without extensive datasets or frequent retraining. It integrates Phonetic Modeling for multilingual versatility and utilizes pre-trained models with optimized pre-processing techniques to ensure low latency and real-time performance. Developed using Python Flask for backend processing, the application offers a user-friendly interface and efficient performance. Audio features are processed using Librosa and FFmpeg, while MinIO provides secure data management. With support for continuous learning, the system adapts to new keywords over time, enhancing accuracy and scalability. Overall, KEYWORD SPOT delivers a lightweight, adaptive, and cost- effective solution for voice-driven systems such as virtual assistants, chatbots, and smart devices, contributing to the advancement of multilingual and intelligent speech recognition technologies.

Introduction

Voice-driven technology has become a key component of modern AI systems, enabling natural human–computer interaction through speech. A crucial element of these systems is Keyword Spotting (KWS), which identifies specific words or commands in continuous audio. Traditional KWS methods require large labeled datasets, struggle with multilingual variations, and must be retrained completely when new keywords are added, making them inefficient and costly.

The proposed project, KEYWORD SPOT – A Few-Shot, Language-Agnostic Keyword Spotting System, addresses these limitations by using Few-Shot Learning (FSL) and Phonetic Modeling to recognize new keywords from only a few examples. By leveraging Prototypical Networks and pre-trained deep learning models, the system achieves real-time, multilingual keyword detection without needing extensive retraining. Implemented as a web application using Python Flask, JavaScript, MinIO, Librosa, and FFmpeg, the project is scalable, modular, and suitable for diverse applications such as voice assistants, customer support automation, security, healthcare, accessibility, and education.

The system aims to handle multilingual inputs, work efficiently with minimal data, maintain low latency, and continually learn new keywords without full-model retraining. The methodology includes audio preprocessing, MFCC and spectrogram-based feature extraction, few-shot model training, and web integration for real-time keyword visualization.

A thorough literature survey highlights foundational works in Few-Shot Learning, deep speech recognition, multilingual keyword detection, and key tools like TensorFlow and Librosa. Research on Prototypical Networks, RNNs, cross-lingual learning, and Transformer models strongly supports the system’s design. Overall, the project contributes to low-resource speech recognition by providing an adaptive, language-independent, and efficient solution for keyword spotting.

References

[1] J. Snell, K. Swersky, and R. Zemel, “Prototypical Networks for Few-Shot Learning,” Advances in Neural Information Processing Systems (NeurIPS), vol. 30, pp. 4077–4087, 2017. [2] A. Graves, A. R. Mohamed, and G. Hinton, “Speech Recognition with Deep Recurrent Neural Networks,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 6645–6649, 2013. [3] T. Banerjee and V. Ramasubramanian, “Few-Shot Learning for Cross-Lingual Isolated Word Recognition in Indian Languages,” ResearchGate Publication, 2021. [4] S. Kumar and R. Singh, “Speech Recognition and Keyword Spotting using Deep Learning for Multilingual Indian Speech Data,” International Journal of Computer Applications, vol. 178, no. 5, pp. 22–29, 2022. [5] T. Javed, et al., “Keyword Spotting for Indian Languages using IndicSUPERB Benchmark,” Indian Institute of Science, Bangalore, 2023. [6] TensorFlow Developers, “TensorFlow: An End-to-End Open Source Machine Learning Platform,” TensorFlow Documentation, 2024. [Online]. [7] Librosa Developers, “Librosa: Python Library for Audio and Music Analysis,” Librosa Documentation, 2024. [Online]. [8] M. McFee et al., “librosa: Audio and Music Signal Analysis in Python,” Proceedings of the 14th Python in Science Conference (SciPy), pp. 18–25, 2015. [9] D. Jurafsky and J. H. Martin, Speech and Language Processing, 3rd ed., Pearson Education, 2023. [10] F. Chollet, Deep Learning with Python, 2nd ed., Manning Publications, 2021.

Copyright

Copyright © 2025 Lakshmi B, Nathiya M, Navyaa S, Mrs. Meena V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.