16,731 research outputs found

    Evaluating Emotional Nuances in Dialogue Summarization

    Full text link
    Automatic dialogue summarization is a well-established task that aims to identify the most important content from human conversations to create a short textual summary. Despite recent progress in the field, we show that most of the research has focused on summarizing the factual information, leaving aside the affective content, which can yet convey useful information to analyse, monitor, or support human interactions. In this paper, we propose and evaluate a set of measures PEmoPEmo, to quantify how much emotion is preserved in dialog summaries. Results show that, summarization models of the state-of-the-art do not preserve well the emotional content in the summaries. We also show that by reducing the training set to only emotional dialogues, the emotional content is better preserved in the generated summaries, while conserving the most salient factual information

    Speech-based recognition of self-reported and observed emotion in a dimensional space

    Get PDF
    The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance

    Employees and customers in call centres: confirmatory and exploratory study

    Get PDF
    Aquest estudi tracta de connectar les disciplines de RH i Màrqueting, i examina el model de service-profit chain (SPC) en el sector de Call Centre, caracteritzat per un servei remot i un negoci basat en la reducció de costos. Les dades s'han col·lectat del Projecte "Global Call Center Project". Hem realitzat dos estudis. En el primer estudi (confirmatori) s'ha emprat una mostra internacional (n = 937). En el segon estudi (exploratori) vam utilitzar una mostra espanyola (n = 109). Els resultats revelen que el model SPC té una aplicació diferent en els call centres. Encara que a nivell general podem confirmar la majoria de les relacions del model, els resultats indiquen que la satisfacció del client és un resultat separat, i no un precursor del rendiment de l'empresa. També trobem una alternativa de mesurar el constructe individual de la satisfacció de l'empleat amb dades disponibles de la organització. Addicionalment, vam descobrir certes discrepàncies en la relació entre la productivitat i la satisfacció dels empleats.El presente estudio es una conexión entre las disciplinas de RH y Marketing, y examina el modelo service-profit chain (SPC) en el sector de Call Centre, caracterizado por un servicio remoto y un negocio basado en la reducción de costes. Los datos se han colectado del Proyecto “Global Call Center Project”. Hemos realizado dos estudios. En el primer estudio (confirmatorio) se ha empleado una muestra internacional (n = 937). En el segundo estudio (exploratorio) utilizamos una muestra española (n = 109). Los resultados revelan que el modelo SPC tiene una aplicación diferente en los call centres. Aunque a nivel general podemos confirmar la mayoría de las relaciones del modelo, los resultados indican que la satisfacción del cliente es un resultado separado, y no un precursor de rendimiento de la empresa. También encontramos una alternativa de medir el constructo individual de satisfacción del empleado con los datos organizacionales disponibles. Adicionalmente, descubrimos ciertas discrepancias en la relación entre la productividad y la satisfacción de los empleados.This thesis is an interface between HR and Marketing discipline, by examining the Service-Profit Chain (SPC) model in the context of call centre, characterized by remote service and cost cutting business models. Data was gathered from the Global Call Centre Project. We carried out two studies. The first one is a confirmatory study, using an international sample (n=937). In the second study we use a Spanish sample (n=109) and carry out an exploratory study. Findings reveal that the SPC model behaves somewhat differently in call centres. Although there is general support for most of the links in the model, the results indicate that customer satisfaction in the call centre industry is a separate outcome, rather than a precursor to company performance. In addition, we found a way to measure the individual level of employee satisfaction construct with organizational available data. We also discovered some discrepancies in the relationship between employee satisfaction and employee productivity

    Does Customers’ Emotion toward Voice-based Service AI Cause Negative Reactions? Empirical Evidence from a Call Center

    Get PDF
    Many companies are introducing voice-based artificial intelligence (AI) into their call centers. Little is known about the relationship between customers’ emotions to voice-based AI service and customers’ negative reactions. This study investigates the link between customers’ emotions toward voice-based AI service and customers’ negative reactions. Our results reveal that customers’ emotion toward voice-based AI service could significantly affect their complaint behavior, and customers’ complaints differ among emotion types. Customers’ negative and positive emotions toward voice-based AI services have a significantly negative and positive effect, respectively, on customer complaint behavior than neutral emotions. We also find that the exchange round of human-computer interaction moderates the effect of the customer emotion by attenuating its effect on customer complaints. This study is the first to empirically test the impact of customers’ emotions toward voice-based AI service on customers’ complaint behavior in the service industry

    Survey on Evaluation Methods for Dialogue Systems

    Get PDF
    In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

    Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions

    Get PDF
    ABSTRACT: In the last years, there has a great progress in automatic speech recognition. The challenge now it is not only recognize the semantic content in the speech but also the called "paralinguistic" aspects of the speech, including the emotions, and the personality of the speaker. This research work aims in the development of a methodology for the automatic emotion recognition from speech signals in non-controlled noise conditions. For that purpose, different sets of acoustic, non-linear, and wavelet based features are used to characterize emotions in different databases created for such purpose
    corecore