Speech-driven Animation with Meaningful Behaviors

Conversational agents (CAs) play an important role in human computer interaction. Creating believable movements for CAs is challenging, since the movements have to be meaningful and natural, reflecting the coupling between gestures and speech. Studies in the past have mainly relied on rule-based or data-driven approaches. Rule-based methods focus on creating meaningful behaviors conveying the underlying message, but the gestures cannot be easily synchronized with speech. Data-driven approaches, especially speech-driven models, can capture the relationship between speech and gestures. However, they create behaviors disregarding the meaning of the message. This study proposes to bridge the gap between these two approaches overcoming their limitations. The approach builds a dynamic Bayesian network (DBN), where a discrete variable is added to constrain the behaviors on the underlying constraint. The study implements and evaluates the approach with two constraints: discourse functions and prototypical behaviors. By constraining on the discourse functions (e.g., questions), the model learns the characteristic behaviors associated with a given discourse class learning the rules from the data. By constraining on prototypical behaviors (e.g., head nods), the approach can be embedded in a rule-based system as a behavior realizer creating trajectories that are timely synchronized with speech. The study proposes a DBN structure and a training approach that (1) models the cause-effect relationship between the constraint and the gestures, (2) initializes the state configuration models increasing the range of the generated behaviors, and (3) captures the differences in the behaviors across constraints by enforcing sparse transitions between shared and exclusive states per constraint. Objective and subjective evaluations demonstrate the benefits of the proposed approach over an unconstrained model.Comment: 13 pages, 12 figures, 5 table

Synthesis of listener vocalizations : towards interactive speech synthesis

Spoken and multi-modal dialogue systems start to use listener vocalizations, such as uh-huh and mm-hm, for natural interaction. Generation of listener vocalizations is one of the major objectives of emotionally colored conversational speech synthesis. Success in this endeavor depends on the answers to three questions: Where to synthesize a listener vocalization? What meaning should be conveyed through the synthesized vocalization? And, how to realize an appropriate listener vocalization with the intended meaning? This thesis addresses the latter question. The investigation starts with proposing a three-stage approach: (i) data collection, (ii) annotation, and (iii) realization. The first stage presents a method to collect natural listener vocalizations from German and British English professional actors in a recording studio. In the second stage, we explore a methodology for annotating listener vocalizations -- meaning and behavior (form) annotation. The third stage proposes a realization strategy that uses unit selection and signal modification techniques to generate appropriate listener vocalizations upon user requests. Finally, we evaluate naturalness and appropriateness of synthesized vocalizations using perception studies. The work is implemented in the open source MARY text-to-speech framework, and it is integrated into the SEMAINE project\u27s Sensitive Artificial Listener (SAL) demonstrator.Dialogsysteme nutzen zunehmend Hörer-Vokalisierungen, wie z.B. a-ha oder mm-hm, für natürliche Interaktion. Die Generierung von Hörer-Vokalisierungen ist eines der zentralen Ziele emotional gefärbter, konversationeller Sprachsynthese. Ein Erfolg in diesem Unterfangen hängt von den Antworten auf drei Fragen ab: Wo bzw. wann sollten Vokalisierungen synthetisiert werden? Welche Bedeutung sollte in den synthetisierten Vokalisierungen vermittelt werden? Und wie können angemessene Hörer-Vokalisierungen mit der intendierten Bedeutung realisiert werden? Diese Arbeit widmet sich der letztgenannten Frage. Die Untersuchung erfolgt in drei Schritten: (i) Korpuserstellung; (ii) Annotation; und (iii) Realisierung. Der erste Schritt präsentiert eine Methode zur Sammlung natürlicher Hörer-Vokalisierungen von deutschen und britischen Profi-Schauspielern in einem Tonstudio. Im zweiten Schritt wird eine Methodologie zur Annotation von Hörer-Vokalisierungen erarbeitet, die sowohl Bedeutung als auch Verhalten (Form) umfasst. Der dritte Schritt schlägt ein Realisierungsverfahren vor, die Unit-Selection-Synthese mit Signalmodifikationstechniken kombiniert, um aus Nutzeranfragen angemessene Hörer-Vokalisierungen zu generieren. Schließlich werden Natürlichkeit und Angemessenheit synthetisierter Vokalisierungen mit Hilfe von Hörtests evaluiert. Die Methode wurde im Open-Source-Sprachsynthesesystem MARY implementiert und in den Sensitive Artificial Listener-Demonstrator im Projekt SEMAINE integriert

Continuous Interaction with a Virtual Human

Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking – and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACE’10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access

Proceedings

Proceedings of the 3rd Nordic Symposium on Multimodal Communication. Editors: Patrizia Paggio, Elisabeth Ahlsén, Jens Allwood, Kristiina Jokinen, Costanza Navarretta. NEALT Proceedings Series, Vol. 15 (2011), vi+87 pp. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/22532

A Study of Accomodation of Prosodic and Temporal Features in Spoken Dialogues in View of Speech Technology Applications

Inter-speaker accommodation is a well-known property of human speech and human interaction in general. Broadly it refers to the behavioural patterns of two (or more) interactants and the effect of the (verbal and non-verbal) behaviour of each to that of the other(s). Implementation of thisbehavior in spoken dialogue systems is desirable as an improvement on the naturalness of humanmachine interaction. However, traditional qualitative descriptions of accommodation phenomena do not provide sufficient information for such an implementation. Therefore, a quantitativedescription of inter-speaker accommodation is required. This thesis proposes a methodology of monitoring accommodation during a human or humancomputer dialogue, which utilizes a moving average filter over sequential frames for each speaker. These frames are time-aligned across the speakers, hence the name Time Aligned Moving Average (TAMA). Analysis of spontaneous human dialogue recordings by means of the TAMA methodology reveals ubiquitous accommodation of prosodic features (pitch, intensity and speech rate) across interlocutors, and allows for statistical (time series) modeling of the behaviour, in a way which is meaningful for implementation in spoken dialogue system (SDS) environments.In addition, a novel dialogue representation is proposed that provides an additional point of view to that of TAMA in monitoring accommodation of temporal features (inter-speaker pause length and overlap frequency). This representation is a percentage turn distribution of individual speakercontributions in a dialogue frame which circumvents strict attribution of speaker-turns, by considering both interlocutors as synchronously active. Both TAMA and turn distribution metrics indicate that correlation of average pause length and overlap frequency between speakers can be attributed to accommodation (a debated issue), and point to possible improvements in SDS “turntaking” behaviour. Although the findings of the prosodic and temporal analyses can directly inform SDS implementations, further work is required in order to describe inter-speaker accommodation sufficiently, as well as to develop an adequate testing platform for evaluating the magnitude ofperceived improvement in human-machine interaction. Therefore, this thesis constitutes a first step towards a convincingly useful implementation of accommodation in spoken dialogue systems

Naturalistic Emotional Speech Corpora with Large Scale Emotional Dimension Ratings

The investigation of the emotional dimensions of speech is dependent on large sets of reliable data. Existing work has been carried out on the creation of emotional speech corpora and the acoustic analysis of emotional speech and this research seeks to buildupon this work while suggesting new methods and areas of potential. A review of the literature determined that a two dimensional emotional model of activation and evaluation was the ideal method for representing the emotional states expressed inspeech. Two case studies were carried out to investigate methods of obtaining naturalunderlying emotional speech in a high quality audio environment, the results of which were used to design a final experimental procedure to elicit natural underlying emotional speech. The speech obtained in this experiment was used in the creation ofa speech corpus that was underpinned by a persistent backend database that incorporated a three-tiered annotation methodology. This methodology was used to comprehensively annotate the metadata, acoustic data and emotional data of the recorded speech. Structuring the three levels of annotation and the assets in a persistent backend database allowed interactive web-based tools to be developed; aweb-based listening tool was developed to obtain a large amount of ratings for the assets that were then written back to the database for analysis. Once a large amount of ratings had been obtained, statistical analysis was used to determine the dimensionalrating for each asset. Acoustic analysis of the underlying emotional speech was then carried out and determined that certain acoustic parameters were correlated with the activation dimension of the dimensional model. This substantiated some of thefindings in the literature review and further determined that spectral energy was strongly correlated with the activation dimension in relation to underlying emotional speech. The lack of a correlation for certain acoustic parameters in relation to the evaluation dimension was also determined, again substantiating some of the findings in the literature.The work contained in this thesis makes a number of contributions to the field: the development of an experimental design to elicit natural underlying emotional speech in a high quality audio environment; the development and implementation of acomprehensive three-tiered corpus annotation methodology; the development and implementation of large scale web based listening tests to rate the emotional dimensions of emotional speech; the determination that certain acoustic parameters are correlated with the activation dimension of a dimensional emotional model inrelation to natural underlying emotional speech and the determination that certain acoustic parameters are not correlated with the evaluation dimension of a twodimensional emotional model in relation to natural underlying emotional speech