Emotion Recognition in Spontaneous and Acted Dialogues

Abstract—In this work, we compare emotion recognition on two types of speech: spontaneous and acted dialogues. Experi-ments were conducted on the AVEC2012 database of spontaneous dialogues and the IEMOCAP database of acted dialogues. We studied the performance of two types of acoustic features for emotion recognition: knowledge-inspired disfluency and non-verbal vocalisation (DIS-NV) features, and statistical Low-Level Descriptor (LLD) based features. Both Support Vector Machines (SVM) and Long Short-Term Memory Recurrent Neural Net-works (LSTM-RNN) were built using each feature set on each emotional database. Our work aims to identify aspects of the data that constrain the effectiveness of models and features. Our results show that the performance of different types of features and models is influenced by the type of dialogue and the amount of training data. Because DIS-NVs are less frequent in acted dialogues than in spontaneous dialogues, the DIS-NV features perform better than the LLD features when recognizing emotions in spontaneous dialogues, but not in acted dialogues. The LSTM-RNN model gives better performance than the SVM model when there is enough training data, but the complex structure of a LSTM-RNN model may limit its performance when there is less training data available, and may also risk over-fitting. Additionally, we find that long distance contexts may be more useful when performing emotion recognition at the word level than at the utterance level. Keywords—emotion recognition, disfluency, LSTM, dialogue I

Automatic Identification of Emotional Information in Spanish TV Debates and Human-Machine Interactions

Automatic emotion detection is a very attractive field of research that can help build more natural human–machine interaction systems. However, several issues arise when real scenarios are considered, such as the tendency toward neutrality, which makes it difficult to obtain balanced datasets, or the lack of standards for the annotation of emotional categories. Moreover, the intrinsic subjectivity of emotional information increases the difficulty of obtaining valuable data to train machine learning-based algorithms. In this work, two different real scenarios were tackled: human–human interactions in TV debates and human–machine interactions with a virtual agent. For comparison purposes, an analysis of the emotional information was conducted in both. Thus, a profiling of the speakers associated with each task was carried out. Furthermore, different classification experiments show that deep learning approaches can be useful for detecting speakers’ emotional information, mainly for arousal, valence, and dominance levels, reaching a 0.7F1-score.The research presented in this paper was conducted as part of the AMIC and EMPATHIC projects, which received funding from the Spanish Minister of Science under grants TIN2017-85854-C4-3-R and PDC2021-120846-C43 and from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 769872. The first author also received a PhD scholarship from the University of the Basque Country UPV/EHU, PIF17/310

Analysis and automatic identification of spontaneous emotions in speech from human-human and human-machine communication

383 p.This research mainly focuses on improving our understanding of human-human and human-machineinteractions by analysing paricipants¿ emotional status. For this purpose, we have developed andenhanced Speech Emotion Recognition (SER) systems for both interactions in real-life scenarios,explicitly emphasising the Spanish language. In this framework, we have conducted an in-depth analysisof how humans express emotions using speech when communicating with other persons or machines inactual situations. Thus, we have analysed and studied the way in which emotional information isexpressed in a variety of true-to-life environments, which is a crucial aspect for the development of SERsystems. This study aimed to comprehensively understand the challenge we wanted to address:identifying emotional information on speech using machine learning technologies. Neural networks havebeen demonstrated to be adequate tools for identifying events in speech and language. Most of themaimed to make local comparisons between some specific aspects; thus, the experimental conditions weretailored to each particular analysis. The experiments across different articles (from P1 to P19) are hardlycomparable due to our continuous learning of dealing with the difficult task of identifying emotions inspeech. In order to make a fair comparison, additional unpublished results are presented in the Appendix.These experiments were carried out under identical and rigorous conditions. This general comparisonoffers an overview of the advantages and disadvantages of the different methodologies for the automaticrecognition of emotions in speech

Continual deep learning via progressive learning

Machine learning is one of several approaches to artificial intelligence. It allows us to build machines that can learn from experience as opposed to being explicitly programmed. Current machine learning formulations are mostly designed for learning and performing a particular task from a tabula rasa using data available for that task. For machine learning to converge to artificial intelligence, in addition to other desiderata, it must be in a state of continual learning, i.e., have the ability to be in a continuous learning process, such that when a new task is presented, the system can leverage prior knowledge from prior tasks, in learning and performing this new task, and augment the prior knowledge with the newly acquired knowledge without having a significant adverse effect on the prior knowledge. Continual learning is key to advancing machine learning and artificial intelligence. Deep learning is a powerful general-purpose approach to machine learning that is able to solve numerous and various tasks with minimal modification. Deep learning extends machine learning, and specially neural networks, to learn multiple levels of distributed representations together with the required mapping function into a single composite function. The emergence of deep learning and neural networks as a generic approach to machine learning, coupled with their ability to learn versatile hierarchical representations, has paved the way for continual learning. The main aim of this thesis is the study and development of a structured approach to continual learning, leveraging the success of deep learning and neural networks. This thesis studies the application of deep learning to a number of supervised learning tasks, and in particular, classification tasks in machine perception, e.g., image recognition, automatic speech recognition, and speech emotion recognition. The relation between the systems developed for these tasks is investigated to illuminate the layer-wise relevance of features in deep networks trained for these tasks via transfer learning, and these independent systems are unified into continual learning systems. The main contribution of this thesis is the construction and formulation of a deep learning framework, denoted progressive learning, that allows a holistic and systematic approach to continual learning. Progressive learning comprises a number of procedures that address the continual learning desiderata. It is shown that, when tasks are related, progressive learning leads to faster learning that converges to better generalization performance using less amounts of data and a smaller number of dedicated parameters, for the tasks studied in this thesis, by accumulating and leveraging knowledge learned across tasks in a continuous manner. It is envisioned that progressive learning is a step towards a fully general continual learning framework