The identification of property neurons in self-supervised speech models significantly enhances interpretability and transparency by providing a clear mapping between specific neurons and distinct speech properties, such as phones, gender, and pitch. This neuron-level analysis allows researchers and practitioners to understand which components of the model are responsible for processing particular features of speech. By pinpointing these neurons, one can visualize and analyze their activation patterns, revealing how the model encodes and retrieves information related to speech properties.
Furthermore, this approach facilitates model editing and pruning, enabling targeted modifications without compromising overall performance. For instance, if a model exhibits bias towards a particular gender, the property neurons associated with gender can be adjusted or pruned to mitigate this bias. This targeted intervention not only improves the model's fairness but also provides insights into the model's decision-making process, making it easier to explain its behavior to end-users and stakeholders. Overall, leveraging property neurons fosters a deeper understanding of the model's inner workings, promoting trust and accountability in self-supervised speech technologies.