  Textual escalation detection has been widely applied to e-commerce companies'
customer service systems to pre-alert and prevent potential conflicts.
Similarly, in public areas such as airports and train stations, where many
impersonal conversations frequently take place, acoustic-based escalation
detection systems are also useful to enhance passengers' safety and maintain
public order. To this end, we introduce a system based on acoustic-lexical
features to detect escalation from speech, Voice Activity Detection (VAD) and
label smoothing are adopted to further enhance the performance in our
experiments. Considering a small set of training and development data, we also
employ transfer learning on several well-known emotional detection datasets,
i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that can be
applied to the escalation detection task. On the development set, our proposed
system achieves 81.5% unweighted average recall (UAR) which significantly
outperforms the baseline with 72.2% UAR.
