  Speech emotion recognition systems have high prediction latency because of
the high computational requirements for deep learning models and low
generalizability mainly because of the poor reliability of emotional
measurements across multiple corpora. To solve these problems, we present a
speech emotion recognition system based on a reductionist approach of
decomposing and analyzing syllable-level features. Mel-spectrogram of an audio
stream is decomposed into syllable-level components, which are then analyzed to
extract statistical features. The proposed method uses formant attention,
noise-gate filtering, and rolling normalization contexts to increase feature
processing speed and tolerance to adversity. A set of syllable-level formant
features is extracted and fed into a single hidden layer neural network that
makes predictions for each syllable as opposed to the conventional approach of
using a sophisticated deep learner to make sentence-wide predictions. The
syllable level predictions help to achieve the real-time latency and lower the
aggregated error in utterance level cross-corpus predictions. The experiments
on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the
method archives real-time latency while predicting with state-of-the-art
cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE.
