Similar results were observed for the EMODB dataset. On the IEMOCAP dataset, the proposed SANN model achieved relative improvements of +6.025% (weighted accuracy) and +5.62% (unweighted accuracy) over the baseline system. A subset of these features was eventually selected using a genetic algorithm approach. A total of 1582 features were extracted from the standard library openSMILE. The proposed network was evaluated on the IEMOCAP and EMODB datasets. The proposed framework jointly optimizes the above two sub-networks to minimize the emotion classification loss and mini-maximize the speaker classification loss. The objective of the GRL layer is to reduce the variance among speakers by maximizing the speaker classification loss. The gradient reversal layer (GRL) was introduced between (a) the layer common to both the primary and auxiliary classifiers and (b) the auxiliary classifier. The DNN framework consists of two sub-networks: one for emotion classification (primary task) and the other for speaker classification (secondary task).
To extract speaker-invariant features, multi-tasking adversarial training of a deep neural network (DNN) is employed. The proposed SANN is used for extracting speaker-invariant and emotion-specific discriminative features for the task of speech emotion recognition.
The result is a speaker adversarial neural network (SANN). the training and testing datasets contain different speakers.
This paper exploits DANN for speaker independent emotion recognition, where the domain corresponds to speakers, i.e. Recently, domain adversarial neural networks (DANN) have delivered promising results for out of domain data.
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