@inproceedings{fucci-etal-2024-explainability,
title = "Explainability for Speech Models: On the Challenges of Acoustic Feature Selection",
author = "Fucci, Dennis and
Savoldi, Beatrice and
Gaido, Marco and
Negri, Matteo and
Cettolo, Mauro and
Bentivogli, Luisa",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.45/",
pages = "373--381",
ISBN = "979-12-210-7060-6",
abstract = "Spurred by the demand for transparency and interpretability in Artificial Intelligence (AI), the field of eXplainable AI (XAI) has experienced significant growth, marked by both theoretical reflections and technical advancements. While various XAI techniques, especially feature attribution methods, have been extensively explored across diverse tasks, their adaptation for the speech modality is lagging behind. We argue that a key factor hindering the diffusion of such methods in speech processing research lies in the complexity of defining interpretable acoustic features. In this paper, we discuss the key challenges in selecting the features for speech explanations. Also in light of existing research, we highlight current gaps and propose future avenues to enhance the depth and informativeness of explanations for speech."
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%0 Conference Proceedings
%T Explainability for Speech Models: On the Challenges of Acoustic Feature Selection
%A Fucci, Dennis
%A Savoldi, Beatrice
%A Gaido, Marco
%A Negri, Matteo
%A Cettolo, Mauro
%A Bentivogli, Luisa
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F fucci-etal-2024-explainability
%X Spurred by the demand for transparency and interpretability in Artificial Intelligence (AI), the field of eXplainable AI (XAI) has experienced significant growth, marked by both theoretical reflections and technical advancements. While various XAI techniques, especially feature attribution methods, have been extensively explored across diverse tasks, their adaptation for the speech modality is lagging behind. We argue that a key factor hindering the diffusion of such methods in speech processing research lies in the complexity of defining interpretable acoustic features. In this paper, we discuss the key challenges in selecting the features for speech explanations. Also in light of existing research, we highlight current gaps and propose future avenues to enhance the depth and informativeness of explanations for speech.
%U https://aclanthology.org/2024.clicit-1.45/
%P 373-381
Markdown (Informal)
[Explainability for Speech Models: On the Challenges of Acoustic Feature Selection](https://aclanthology.org/2024.clicit-1.45/) (Fucci et al., CLiC-it 2024)
ACL