Speech Emotion Recognition with Data Augmentation and Layer-wise Learning Rate Adjustment.
Year: 2018
Type: preprint
Abstract: In this work, we design a neural network for recognizing emotions in speech, using the standard 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. Applying techniques of data augmentation, layer-wise learning rate adjustment and batch normalization, we obtain highly competitive results, with 64.5% weighted accuracy and 61.7% unweighted accuracy on four emotions. Moreover, we show that the model performance is strongly correlated with the labeling confidence, which highlights a fundamental difficulty in emotion recognition.
Source: arXiv (Cornell University)
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2785737646