Keyword spotting systems often struggle to generalize to a diverse population with various accents and age groups. To address this challenge, we propose a novel approach that integrates speaker information into keyword spotting using Feature-wise Linear Modulation (FiLM), a recent method for learning from multiple sources of information. We explore both Text-Dependent and Text-Independent speaker recognition systems to extract speaker information, and we experiment on extracting this information from both the input audio and pre-enrolled user audio. We evaluate our systems on a diverse dataset and achieve a substantial improvement in keyword detection accuracy, particularly among underrepresented speaker groups. Moreover, our proposed approach only requires a small 1% increase in the number of parameters, with a minimum impact on latency and computational cost, which makes it a practical solution for real-world applications.
@article{arxiv.2311.03419,
title = {Personalizing Keyword Spotting with Speaker Information},
author = {Beltrán Labrador and Pai Zhu and Guanlong Zhao and Angelo Scorza Scarpati and Quan Wang and Alicia Lozano-Diez and Alex Park and Ignacio López Moreno},
journal= {arXiv preprint arXiv:2311.03419},
year = {2023}
}