Personalizing Keyword Spotting with Speaker Information
Beltrán Labrador Pai Zhu Guanlong Zhao Angelo Scorza Scarpati Quan Wang Alicia Lozano-Diez Alex Park Ignacio López Moreno
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.