Year: 2025
Type: conference-paper
Abstract: 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 that allows models to learn from different data inputs and features. 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. Evaluating our systems on a diverse dataset, our primary approach yields a notable 2.6% relative improvement on Equal Error Rate overall, particularly improving performance by 5.9% for children under 12 years old and up to 24% for 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.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
Awards: G121809054, G1325136967, G1622782223, G1650125035, G1855990336, G2176187742, G2262748287, G2585926585, G2660406951, G3480869486, G425668148, G451917667, G5282425219, G5421974903, G586857820, G5967599077, G6178709868, G6298273575, G7084143925, G7456023404, G7863371703, G8181105605, G8497050386
OpenAlex ID: W4408354069