Speech Emotion Recognition (SER) has been shown to benefit from many of the recent advances in deep learning, including recurrent based and attention based neural network architectures as well. Nevertheless, performance still falls short of that of humans. In this work, we investigate whether SER could benefit from the self-attention and global windowing of the transformer model. We show on the IEMOCAP database that this is indeed the case. Finally, we investigate whether using the distribution of, possibly conflicting, annotations in the training data, as soft targets could outperform a majority voting. We prove that this performance increases with the agreement level of the annotators.
A class of nonlinear finite-horizon optimal control problems is studied. We propose a solution based on an iterative strategy that relies on the linearization of the nonlinear dynamics and on the construction of the corresponding time-varying Hamiltonian dynamics. Differently from existing methods that hinge upon similar tools, the proposed strategy hinges upon the solution to a linear initial value problem and does not require at each step the (numerical) solution of a two-point boundary value problem or of a time-varying Riccati equation. The result is achieved by exploiting a time-varying change of coordinates with the objective of obtaining a constant optimal costate in the transformed coordinates.
In sequence modeling tasks the token order matters, but this information can be partially lost due to the discretization of the sequence into data points. In this paper, we study the imbalance between the way certain token pairs are included in data points and others are not. We denote this a token order imbalance (TOI) and we link the partial sequence information loss to a diminished performance of the system as a whole, both in text and speech processing tasks. We then provide a mechanism to leverage the full token order information-Alleviated TOI-by iteratively overlapping the token composition of data points. For recurrent networks, we use prime numbers for the batch size to avoid redundancies when building batches from overlapped data points. The proposed method achieved state of the art performance in both text and speech related tasks.
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