Auditory prominence is defined as when an acoustic segment is made salient in its context. Prominence is one of the prosodic functions that has been shown to be strongly correlated with facial movements. In this work, we investigate the effects of facial prominence cues, in terms of gestures, when synthesized on animated talking heads. In the first study, a speech intelligibility experiment is conducted, speech quality is acoustically degraded and the fundamental frequency is removed from the signal, then the speech is presented to 12 subjects through a lip synchronized talking head carrying head-nods and eyebrows raise gestures, which are synchronized with the auditory prominence. The experiment shows that presenting prominence as facial gestures significantly increases speech intelligibility compared to when these gestures are randomly added to speech. We also present a follow-up study examining the perception of the behavior of the talking heads when gestures are added over pitch accents. Using eye-gaze tracking technology and questionnaires on 10 moderately hearing impaired subjects, the results of the gaze data show that users look at the face in a similar fashion to when they look at a natural face when gestures are coupled with pitch accents opposed to when the face carries no gestures. From the questionnaires, the results also show that these gestures significantly increase the naturalness and the understanding of the talking head.

This study aims at automatically classifying levels of acoustic prominence on a dataset of 200 Swedish sentences of read speech by one male native speaker. Each word in the sentences was categorized by four speech experts into one of three groups depending on the level of prominence perceived. Six acoustic features at a syllable level and seven features at a word level were used. Two machine learning algorithms, namely Support Vector Machines (SVM) and memory based Learning (MBL) were trained to classify the sentences into their respective classes. The MBL gave an average word level accuracy of 69.08% and the SVM gave an average accuracy of 65.17 % on the test set. These values were comparable with the average accuracy of the human annotators with respect to the average annotations. In this study, word duration was found to be the most important feature required for classifying prominence in Swedish read speech

This paper presents an approach to estimating word level prominence in Swedish using syllable level features. The paper discusses the mismatch problem of annotations between word level perceptual prominence and its acoustic correlates, context, and data scarcity. 200 sentences are annotated by 4 speech experts with prominence on 3 levels. A linear model for feature extraction is proposed on a syllable level features, and weights of these features are optimized to match word level annotations. We show that using syllable level features and estimating weights for the acoustic correlates to minimize the word level estimation error gives better detection accuracy compared to word level features, and that both features exceed the baseline accuracy.

The perception of gaze plays a crucial role in human-human interaction. Gaze has been shown to matter for a number of aspects of communication and dialogue, especially for managing the flow of the dialogue and participant attention, for deictic referencing, and for the communication of attitude. When developing embodied conversational agents (ECAs) and talking heads, modeling and delivering accurate gaze targets is crucial. Traditionally, systems communicating through talking heads have been displayed to the human conversant using 2D displays, such as flat monitors. This approach introduces severe limitations for an accurate communication of gaze since 2D displays are associated with several powerful effects and illusions, most importantly the Mona Lisa gaze effect, where the gaze of the projected head appears to follow the observer regardless of viewing angle. We describe the Mona Lisa gaze effect and its consequences in the interaction loop, and propose a new approach for displaying talking heads using a 3D projection surface (a physical model of a human head) as an alternative to the traditional flat surface projection. We investigate and compare the accuracy of the perception of gaze direction and the Mona Lisa gaze effect in 2D and 3D projection surfaces in a five subject gaze perception experiment. The experiment confirms that a 3Dprojection surface completely eliminates the Mona Lisa gaze effect and delivers very accurate gaze direction that is independent of the observer's viewing angle. Based on the data collected in this experiment, we rephrase the formulation of the Mona Lisa gaze effect. The data, when reinterpreted, confirms the predictions of the new model for both 2D and 3D projection surfaces. Finally, we discuss the requirements on different spatially interactive systems in terms of gaze direction, and propose new applications and experiments for interaction in a human-ECA and a human-robot settings made possible by this technology.

In a previous experiment we found that the perception of gazefrom an animated agent on a two-dimensional display suffersfrom the Mona Lisa effect, which means that exclusive mutual gaze cannot be established if there is more than one observer. By using a three-dimensional projection surface, this effect can be eliminated. In this study, we investigate whether this difference also holds for the turn-taking behaviour of subjects interacting with the animated agent in a multi-party dialogue. We present a Wizard-of-Oz experiment where five subjects talk toan animated agent in a route direction dialogue. The results show that the subjects to some extent can infer the intended target of the agent’s questions, in spite of the Mona Lisa effect, but that the accuracy of gaze when it comes to selecting an addressee is still significantly lower in the 2D condition, ascompared to the 3D condition. The response time is also significantly longer in the 2D condition, indicating that the inference of intended gaze may require additional cognitive efforts.

In this chapter, we first present a summary of findings from two previous studies on the limitations of using flat displays with embodied conversational agents (ECAs) in the contexts of face-to-face human-agent interaction. We then motivate the need for a three dimensional display of faces to guarantee accurate delivery of gaze and directional movements and present Furhat, a novel, simple, highly effective, and human-like back-projected robot head that utilizes computer animation to deliver facial movements, and is equipped with a pan-tilt neck. After presenting a detailed summary on why and how Furhat was built, we discuss the advantages of using optically projected animated agents for interaction. We discuss using such agents in terms of situatedness, environment, context awareness, and social, human-like face-to-face interaction with robots where subtle nonverbal and social facial signals can be communicated. At the end of the chapter, we present a recent application of Furhat as a multimodal multiparty interaction system that was presented at the London Science Museum as part of a robot festival,. We conclude the paper by discussing future developments, applications and opportunities of this technology.

Back projecting a computer animated face, onto a three dimensional static physical model of a face, is a promising technology that is gaining ground as a solution to building situated, flexible and human-like robot heads. In this paper, we first briefly describe Furhat, a back projected robot head built for the purpose of multimodal multiparty human-machine interaction, and its benefits over virtual characters and robotic heads; and then motivate the need to investigating the contribution to speech intelligibility Furhat's face offers. We present an audio-visual speech intelligibility experiment, in which 10 subjects listened to short sentences with degraded speech signal. The experiment compares the gain in intelligibility between lip reading a face visualized on a 2D screen compared to a 3D back-projected face and from different viewing angles. The results show that the audio-visual speech intelligibility holds when the avatar is projected onto a static face model (in the case of Furhat), and even, rather surprisingly, exceeds it. This means that despite the movement limitations back projected animated face models bring about; their audio visual speech intelligibility is equal, or even higher, compared to the same models shown on flat displays. At the end of the paper we discuss several hypotheses on how to interpret the results, and motivate future investigations to better explore the characteristics of visual speech perception 3D projected faces.

Accurate human perception of robots' gaze direction is crucial for the design of a natural and fluent situated multimodal face-to-face interaction between humans and machines. In this paper, we present an experiment targeted at quantifying the effects of different gaze cues synthesized using the Furhat back-projected robot head, on the accuracy of perceived spatial direction of gaze by humans using 18 test subjects. The study first quantifies the accuracy of the perceived gaze direction in a human-human setup, and compares that to the use of synthesized gaze movements in different conditions: viewing the robot eyes frontal or at a 45 degrees angle side view. We also study the effect of 3D gaze by controlling both eyes to indicate the depth of the focal point (vergence), the use of gaze or head pose, and the use of static or dynamic eyelids. The findings of the study are highly relevant to the design and control of robots and animated agents in situated face-to-face interaction.