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.

通过整合说话者信息到关键词检测系统，使用 Feature-wise Linear Modulation（FiLM）方法从多个信息源进行学习，我们在多样化数据集上实验并取得了显著提高的关键词检测准确性，特别是在不平衡说话者群体中，还能在参数数量上只增加 1% 的情况下，最小程度影响延迟和计算成本，使其成为实际应用中的可行解决方案。