The recognition performance of a classifier is affected by various aspects. A huge influence is given by the input data pre-processing. In the current paper we analysed the relation between different normalisation methods for emotionally coloured speech samples deriving general trends to be considered during data pre-processing. From the best of our knowledge, various normalisation approaches are used in the spoken affect recognition community but so far no multi-corpus comparison was conducted. Therefore, well-known methods from literature were compared in a larger study based on nine benchmark corpora, where within each data set a leave-one-speaker-out validation strategy was applied. As normalisation approaches, we investigated standardisation, range normalisation, and centering. These were tested in two possible options: (1) The normalisation parameters were estimated on the whole data set and (2) we obtained the parameters by using emotionally neutral samples only. For classification Support Vector Machines with linear and polynomial kernels as well as Random Forest were used as representatives of classifiers handling input material in different ways. Besides further recommendations we showed that standardisation leads to a significant improvement of the recognition performance. It is also discussed when and how to apply normalisation methods.

Nowadays, individuals can be overwhelmed by a huge number of documents being present in daily life. Capturing the necessary details is often a challenge. Therefore, it is rather important to summarize documents to obtain the main information quickly. There currently exist automatic approaches to this task, but their quality is often not properly assessed. State-of-the-art metrics rely on human-generated summaries as a reference for the evaluation. If no reference is given, the assessment will be challenging. Therefore, in the absence of human-generated reference summaries, we investigated an alternative approach to how machine-generated summaries can be evaluated. For this, we focus on the original text or document to retrieve a metric that allows a direct evaluation of automatically generated summaries. This approach is particularly helpful in cases where it is difficult or costly to find reference summaries. In this paper, we present a novel metric called Summary Score without Reference—SUSWIR—which is based on four factors already known in the text summarization community: Semantic Similarity, Redundancy, Relevance, and Bias Avoidance Analysis, overcoming drawbacks of common metrics. Therefore, we aim to close a gap in the current evaluation environment for machine-generated text summaries. The novel metric is introduced theoretically and tested on five datasets from their respective domains. The conducted experiments yielded noteworthy outcomes, employing the utilization of SUSWIR.

This review presents, in a tutorial-like manner, the current state of research in group affects. We focus on the automatic processing of affects and their estimation, which is, of course, based on foundations in social sciences regarding multiple modalities. In addition, we highlight recent developments in methods and classification approaches. Further, a collection of suitable corpora for affect estimation is presented. Based on the literature, we discuss current achievements in relation to parameters that influence the processing of groups. Finally, in the sense of a road map, different perspectives on further research are presented.