# Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation Source: [Speech Emotion Recognition with Multiscale Area Attention and Data Augmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:55:51.498Z Authors: Mingke Xu, Fan Zhang, Xiaodong Cui, Wei Zhang Published: 2021-02-03T00:39:09.000Z ## Abstract In Speech Emotion Recognition (SER), emotional characteristics often appear in diverse forms of energy patterns in spectrograms. Typical attention neural network classifiers of SER are usually optimized on a fixed attention granularity. In this paper, we apply multiscale area attention in a deep convolutional neural network to attend emotional characteristics with varied granularities and therefore the classifier can benefit from an ensemble of attentions with different scales. To deal with data sparsity, we conduct data augmentation with vocal tract length perturbation (VTLP) to improve the generalization capability of the classifier. Experiments are carried out on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset. We achieved 79.34% weighted accuracy (WA) and 77.54% unweighted accuracy (UA), which, to the best of our knowledge, is the state of the art on this dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Speech Emotion Recognition on MSP-IMPROV (6-fold session-independent cross-validation)](): Speech Emotion Recognition; MSP-IMPROV (6-fold session-independent cross-validation); Weighted Accuracy: 47.9. ## Original sources and implementation [Original paper]() [PDF]()