  Despite notable progress, speech emotion recognition (SER) remains
challenging due to the intricate and ambiguous nature of speech emotion,
particularly in wild world. While current studies primarily focus on
recognition and generalization abilities, our research pioneers an
investigation into the reliability of SER methods in the presence of semantic
data shifts and explores how to exert fine-grained control over various
attributes inherent in speech signals to enhance speech emotion modeling. In
this paper, we first introduce MSAC-SERNet, a novel unified SER framework
capable of simultaneously handling both single-corpus and cross-corpus SER.
Specifically, concentrating exclusively on the speech emotion attribute, a
novel CNN-based SER model is presented to extract discriminative emotional
representations, guided by additive margin softmax loss. Considering
information overlap between various speech attributes, we propose a novel
learning paradigm based on correlations of different speech attributes, termed
Multiple Speech Attribute Control (MSAC), which empowers the proposed SER model
to simultaneously capture fine-grained emotion-related features while
mitigating the negative impact of emotion-agnostic representations.
Furthermore, we make a first attempt to examine the reliability of the
MSAC-SERNet framework using out-of-distribution detection methods. Experiments
on both single-corpus and cross-corpus SER scenarios indicate that MSAC-SERNet
not only consistently outperforms the baseline in all aspects, but achieves
superior performance compared to state-of-the-art SER approaches.
