# Direct Modelling of Speech Emotion from Raw Speech

## TL;DR

This paper introduces a novel deep learning model combining parallel CNN layers with LSTM for raw speech emotion recognition, outperforming traditional feature-based methods on benchmark datasets.

## Contribution

It proposes a parallel CNN architecture to better capture temporal resolutions in raw speech for emotion recognition, enhancing existing deep learning approaches.

## Key findings

- Model achieves comparable performance to hand-engineered feature methods.
- Parallel CNN layers improve contextual modeling in raw speech.
- Results validated on IEMOCAP and MSP-IMPROV datasets.

## Abstract

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not always guaranteed to be the best in a statistical modelling framework where the end goal is for example emotion classification. This has fuelled the emerging trend of learning representations from raw speech especially using deep learning neural networks. In particular, a combination of Convolution Neural Networks (CNNs) and Long Short Term Memory (LSTM) have gained great traction for the intrinsic property of LSTM in learning contextual information crucial for emotion recognition; and CNNs been used for its ability to overcome the scalability problem of regular neural networks. In this paper, we show that there are still opportunities to improve the performance of emotion recognition from the raw speech by exploiting the properties of CNN in modelling contextual information. We propose the use of parallel convolutional layers to harness multiple temporal resolutions in the feature extraction block that is jointly trained with the LSTM based classification network for the emotion recognition task. Our results suggest that the proposed model can reach the performance of CNN trained with hand-engineered features from both IEMOCAP and MSP-IMPROV datasets.