  Speech Emotion Recognition (SER) aims to help the machine to understand
human's subjective emotion from only audio information. However, extracting and
utilizing comprehensive in-depth audio information is still a challenging task.
In this paper, we propose an end-to-end speech emotion recognition system using
multi-level acoustic information with a newly designed co-attention module. We
firstly extract multi-level acoustic information, including MFCC, spectrogram,
and the embedded high-level acoustic information with CNN, BiLSTM and wav2vec2,
respectively. Then these extracted features are treated as multimodal inputs
and fused by the proposed co-attention mechanism. Experiments are carried on
the IEMOCAP dataset, and our model achieves competitive performance with two
different speaker-independent cross-validation strategies. Our code is
available on GitHub.
