Study of Dense Network Approaches for Speech Emotion Recognition

Mohammed Abdelwahab · Carlos Busso

53 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Deep neural networks have been proven to be very effective in various classification problems and show great promise for emotion recognition from speech. Studies have proposed various architectures that further improve the performance of emotion recognition systems. However, there are still various open questions regarding the best approach to building a speech emotion recognition system. Would the system's performance improve if we have more labeled data? How much do we benefit from data augmentation? What activation and regularization schemes are more beneficial? How does the depth of the network affect the performance? We are collecting the MSP-Podcast corpus, a large dataset with over 30 hours of data, which provides an ideal resource to address these questions. This study explores various dense architectures to predict arousal, valence and dominance scores. We investigate varying the training set size, width, and depth of the network, as well as the activation functions used during training. We also study the effect of data augmentation on the network's performance. We find that bigger training set improves the performance. Batch normalization is crucial to achieving a good performance for deeper networks. We do not observe significant differences in the performance in residual networks compared to dense networks.

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References

2014CREMA-D: Crowd-Sourced Emotional Multimodal Actors Dataset 2014Speech emotion recognition using deep neural network and extreme learning machine 2013The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism 2017Evaluating deep learning architectures for Speech Emotion Recognition 2015EmoNets: Multimodal deep learning approaches for emotion recognition in video 2017Building Naturalistic Emotionally Balanced Speech Corpus by Retrieving Emotional Speech from Existing Podcast Recordings 2015A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space 2015Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks

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2021Convolutional-Recurrent Neural Networks With Multiple Attention Mechanisms for Speech Emotion Recognition 2019Active Learning for Speech Emotion Recognition Using Deep Neural Network

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