@inproceedings{lopez-etal-2026-ok,
title = "``{OK} Aura, Be Fair with Me'': Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection",
author = "L{\'o}pez, Fernando and
Delgado-Santos, Paula and
G{\'o}mez, Pablo and
Solans, David and
Luque, Jordi",
editor = "Hosseini-Kivanani, Nina and
Brutti, Alessio and
Matassoni, Marco and
Dowerah, Sandipana and
Liga, Davide and
Schommer, Christoph",
booktitle = "Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis ({SPEAKABLE}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.speakable-1.6/",
doi = "10.63317/3sgj2rai4uv8",
pages = "47--58",
abstract = "Voice-based interfaces are widely used; however, achieving fair Wake-up Word detection across diverse speaker populations remains a critical challenge due to persistent demographic biases. This study evaluates the effectiveness of demographics-agnostic training techniques in mitigating performance disparities among speakers of varying sex, age, and accent. We utilize the OK Aura database for our experiments, employing a training methodology that excludes demographic labels, which are reserved for evaluation purposes. We explore (i) data augmentation techniques to enhance model generalization and (ii) Knowledge Distillation of pre-trained foundational speech models. The experimental results indicate that these demographics-agnostic training techniques markedly reduce demographic bias, leading to a more equitable performance profile across different speaker groups. Specifically, one of the evaluated techniques achieves a Predictive Disparity reduction of 39.94{\%} for sex, 83.65{\%} for age, and 40.48{\%} for accent when compared to the baseline. This study highlights the effectiveness of label-agnostic methodologies in fostering fairness in Wake-up Word detection."
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<abstract>Voice-based interfaces are widely used; however, achieving fair Wake-up Word detection across diverse speaker populations remains a critical challenge due to persistent demographic biases. This study evaluates the effectiveness of demographics-agnostic training techniques in mitigating performance disparities among speakers of varying sex, age, and accent. We utilize the OK Aura database for our experiments, employing a training methodology that excludes demographic labels, which are reserved for evaluation purposes. We explore (i) data augmentation techniques to enhance model generalization and (ii) Knowledge Distillation of pre-trained foundational speech models. The experimental results indicate that these demographics-agnostic training techniques markedly reduce demographic bias, leading to a more equitable performance profile across different speaker groups. Specifically, one of the evaluated techniques achieves a Predictive Disparity reduction of 39.94% for sex, 83.65% for age, and 40.48% for accent when compared to the baseline. This study highlights the effectiveness of label-agnostic methodologies in fostering fairness in Wake-up Word detection.</abstract>
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%0 Conference Proceedings
%T “OK Aura, Be Fair with Me”: Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection
%A López, Fernando
%A Delgado-Santos, Paula
%A Gómez, Pablo
%A Solans, David
%A Luque, Jordi
%Y Hosseini-Kivanani, Nina
%Y Brutti, Alessio
%Y Matassoni, Marco
%Y Dowerah, Sandipana
%Y Liga, Davide
%Y Schommer, Christoph
%S Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F lopez-etal-2026-ok
%X Voice-based interfaces are widely used; however, achieving fair Wake-up Word detection across diverse speaker populations remains a critical challenge due to persistent demographic biases. This study evaluates the effectiveness of demographics-agnostic training techniques in mitigating performance disparities among speakers of varying sex, age, and accent. We utilize the OK Aura database for our experiments, employing a training methodology that excludes demographic labels, which are reserved for evaluation purposes. We explore (i) data augmentation techniques to enhance model generalization and (ii) Knowledge Distillation of pre-trained foundational speech models. The experimental results indicate that these demographics-agnostic training techniques markedly reduce demographic bias, leading to a more equitable performance profile across different speaker groups. Specifically, one of the evaluated techniques achieves a Predictive Disparity reduction of 39.94% for sex, 83.65% for age, and 40.48% for accent when compared to the baseline. This study highlights the effectiveness of label-agnostic methodologies in fostering fairness in Wake-up Word detection.
%R 10.63317/3sgj2rai4uv8
%U https://aclanthology.org/2026.speakable-1.6/
%U https://doi.org/10.63317/3sgj2rai4uv8
%P 47-58
Markdown (Informal)
["OK Aura, Be Fair with Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection](https://aclanthology.org/2026.speakable-1.6/) (López et al., SPEAKABLE 2026)
ACL
- Fernando López, Paula Delgado-Santos, Pablo Gómez, David Solans, and Jordi Luque. 2026. "OK Aura, Be Fair with Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection. In Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026, pages 47–58, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).