Emotion detection in textual data has received growing interest in recent years, as it is pivotal for developing empathetic human-computer interaction systems.This paper introduces a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions.Initially, we establish a baseline by training a transformer-based model for standard emotion classification, achieving state-of-the-art performance. We argue that not all misclassifications are of the same importance, as there are perceptual similarities among emotional classes.We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels.Finally, we propose a method that performs ordinal classification in the two-dimensional emotion space, considering both valence and arousal scales.The results show that our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification.

Creating new voices for a TTS system often requires a costly procedure of designing and recording an audio corpus, a time consuming and effort intensive task. Using publicly available audiobooks as the raw material of a spoken corpus for such systems creates new perspectives regarding the possibility of creating new synthetic voices quickly and with limited effort. This paper addresses the issue of creating new synthetic voices based on audiobook data in an automated method. As an audiobook includes several types of speech, such as narration, character playing etc., special care is given in identifying the data subset that leads to a more neutral and general purpose synthetic voice. The main goal is to identify and address the effect the audiobook speech diversity on the resulting TTS system. Along with the methodology for coping with this diversity in the speech data, we also describe a set of experiments performed in order to investigate the efficiency of different approaches for automatic data pruning. Further plans for exploiting the diversity of the speech incorporated in an audiobook are also described in the final section and conclusions are drawn.

This paper presents research on “Greeklish,” that is, a transliteration of Greek using the Latin alphabet, which is used frequently in Greek e-mail communication. Greeklish is not standardized and there are a number of competing conventions co-existing in communication, based on personal preferences regarding similarities between Greek and Latin letters in shape, sound, or keyboard position. Our research has led to the development of “All Greek to me!” the first automatic transliteration system that can cope with any type of Greeklish. In this paper we first present previous research on Greeklish, describing other approaches that have attempted to deal with the same problems. We then provide a brief description of our approach, illustrating the functional flowchart of our system and the main ideas that underlie it. We present measures of system performance, based on about a year’s worth of usage as a public web service, and preliminary research, based on the same corpus, on the use of Greeklish and the trends in preferred Latin-Greek letter mapping. We evaluate the consistency of different transliteration patterns among users as well as the within-user consistency based on coherent principles. Finally we outline planned future research to further understand the use of Greeklish and improve “All Greek to me!” to function reliably embedded in integrated communication platforms bridging e-mail to mobile telephony and ubiquitous connectivity.