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arXiv · 1802.05630

CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation

Caroline Etienne·Guillaume Fidanza·Andrei Petrovskii·Laurence Devillers·Benoit Schmauch
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Abstract

In this work we design a neural network for recognizing emotions in speech, using the IEMOCAP dataset. Following the latest advances in audio analysis, we use an architecture involving both convolutional layers, for extracting high-level features from raw spectrograms, and recurrent ones for aggregating long-term dependencies. We examine the techniques of data augmentation with vocal track length perturbation, layer-wise optimizer adjustment, batch normalization of recurrent layers and obtain highly competitive results of 64.5% for weighted accuracy and 61.7% for unweighted accuracy on four emotions.

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BibTeXRIS

Caroline Etienne, Guillaume Fidanza, Andrei Petrovskii, Laurence Devillers, Benoit Schmauch. 2018-02-15. CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation. https://doi.org/10.21437/smm.2018-5

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