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Today's Speech & Audio Research Top Papers
Wednesday, November 5, 2025
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Introduces a deep state space model for condition-invariant fMRI decoding of speech intelligibility. Achieves state-of-the-art performance, demonstrating condition-invariant neural codes across diverse listening environments for speech processing.
Develops a Gaussian process framework to learn interaction kernels in multispecies particle systems from trajectory data. Establishes convergence theory for single-species systems and extends to second-order models, enabling better multiscale modeling.
Proposes EchoLSTM, a self-reflective recurrent network using output-conditioned gating to stabilize long-range memory. Enhances memory retention in sequences with noisy or misleading information, improving performance over standard LSTMs.
Instantiates the SCoBots framework for interpretable neurosymbolic RL agents, decomposing tasks into interpretable representations. Addresses shortcut learning in deep RL by using object-centric states, improving generalization.
Presents ProtoTSNet for interpretable multivariate time series classification using prototypical parts. Enhances ProtoPNet for critical domains like industry and medicine, providing accurate and understandable decisions.
Introduces Characteristic Function Loss (CFL) for domain alignment in machine learning. Addresses distribution shift by learning models that perform well in real-world scenarios, improving robustness.
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