DNN-Based Feature Extraction for Conflict Intensity Estimation From Speech

Over past few years, there has been an increasing need to extract nonlinguistic information from audio sources. This trend has created a new area in speech technology known as computational paralinguistics. A task belonging to this area is to estimate the intensity of conflicts arising in speech recordings, based only on the audio information. It was shown that the human comprehension of conflict intensity is closely related to speaker overlap; that is, when multiple persons are speaking at the same time. This type of information can also aid automated conflict intensity estimation. In this study, we propose a simple, DNN-based feature extraction step, and show that this approach is superior to those introduced in the literature so far: By combining our results with an efficient greedy feature selection algorithm, we were able to outperform all previous results on the SSPNet conflict dataset, achieving a correlation coefficient of 0.856 on the test set.