  Speech Emotion Recognition (SER) is crucial in human-machine interactions.
Mainstream approaches utilize Convolutional Neural Networks or Recurrent Neural
Networks to learn local energy feature representations of speech segments from
speech information, but struggle with capturing global information such as the
duration of energy in speech. Some use Transformers to capture global
information, but there is room for improvement in terms of parameter count and
performance. Furthermore, existing attention mechanisms focus on spatial or
channel dimensions, hindering learning of important temporal information in
speech. In this paper, to model local and global information at different
levels of granularity in speech and capture temporal, spatial and channel
dependencies in speech signals, we propose a Speech Emotion Recognition network
based on CNN-Transformer and multi-dimensional attention mechanisms.
Specifically, a stack of CNN blocks is dedicated to capturing local information
in speech from a time-frequency perspective. In addition, a time-channel-space
attention mechanism is used to enhance features across three dimensions.
Moreover, we model local and global dependencies of feature sequences using
large convolutional kernels with depthwise separable convolutions and
lightweight Transformer modules. We evaluate the proposed method on IEMOCAP and
Emo-DB datasets and show our approach significantly improves the performance
over the state-of-the-art methods.
