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An important task in human-computer interaction is to rank speech samples according to their expressive content. A preference learning framework is appropriate for obtaining an emotional rank for a set of speech samples. However, obtaining reliable labels for training a preference learning framework is a challenging task. Most existing databases provide sentence-level absolute attribute scores annotated by multiple raters, which have to be transformed to obtain preference labels. Previous studies have shown that evaluators anchor their absolute assessments on previously annotated samples. Hence, this study proposes a novel formulation for obtaining preference learning labels by only considering annotation trends assigned by a rater to consecutive samples within an evaluation session. The experiments show that the use of the proposed anchor-based ordinal labels leads to significantly better performance than models trained using existing alternative labels. 

The field of speech emotion recognition (SER) aims to create scientifically rigorous systems that can reliably characterize emotional behaviors expressed in speech. A key aspect for building SER systems is to obtain emotional data that is both reliable and reproducible for practitioners. However, academic researchers encounter difficulties in accessing or collecting naturalistic, large-scale, reliable emotional recordings. Also, the best practices for data collection are not necessarily described or shared when presenting emotional corpora. To address this issue, the paper proposes the creation of an affective naturalistic database consortium (AndC) that can encourage multidisciplinary cooperation among researchers and practitioners in the field of affective computing. This paper’s contribution is twofold. First, it proposes the design of the AndC with a customizable-standard framework for intelligently-controlled emotional data collection. The focus is on leveraging naturalistic spontaneous record- ings available on audio-sharing websites. Second, it presents as a case study the development of a naturalistic large-scale Taiwanese Mandarin podcast corpus using the customizable- standard intelligently-controlled framework. The AndC will en- able research groups to effectively collect data using the provided pipeline and to contribute with alternative algorithms or data collection protocols. 

The performance of facial expression recognition (FER) systems has improved with recent advances in machine learning. While studies have reported impressive accuracies in detecting emotion from posed expressions in static images, there are still important challenges in developing FER systems for videos, especially in the presence of speech. Speech articulation modulates the orofacial area, changing the facial appearance. These facial movements induced by speech introduce noise, reducing the performance of an FER system. Solving this problem is important if we aim to study more naturalistic environment or applications in the wild. We propose a novel approach to compensate for lexical information that does not require phonetic information during inference. The approach relies on a style extractor model, which creates emotional-to-neutral transformations. The transformed facial representations are spatially contrasted with the original faces, highlighting the emotional information conveyed in the video. The results demonstrate that adding the proposed style extractor model to a dynamic FER system improves the performance by 7% (absolute) compared to a similar model with no style extractor. This novel feature representation also improves the generaliza- tion of the model.