This paper presents a unique dataset consisting of 20 recordings of the same musical piece, conducted with 4 different musical intentions in mind. The upper body and baton motion of a professional conductor was recorded, as well as the sound of each instrument in a professional string quartet following the conductor. The dataset is made available for benchmarking of motion recognition algorithms. An HMM-based emotion intent classification method is trained with subsets of the data, and classification of other subsets of the data show firstly that the motion of the baton communicates energetic intention to a high degree, secondly, that the conductor’s torso, head and other arm conveys calm intention to a high degree, and thirdly, that positive vs negative sentiments are communicated to a high degree through other channels than the body and baton motion – most probably, through facial expression and muscle tension conveyed through articulated hand and finger motion. The long-term goal of this work is to develop a computer model of the entire conductor-orchestra communication pro- cess; the studies presented here indicate that computer modeling of the conductor-orchestra communication is feasible.

Speech and gesture co-occur in spontaneous dialogue in a highly complex fashion. There is a large variability in the motion that people exhibit during a dialogue, and different kinds of motion occur during different states of the interaction. A wide range of multimodal interface applications, for example in the fields of virtual agents or social robots, can be envisioned where it is important to be able to automatically identify gestures that carry information and discriminate them from other types of motion. While it is easy for a human to distinguish and segment manual gestures from a flow of multimodal information, the same task is not trivial to perform for a machine. In this paper we present a method to automatically segment and label gestural units from a stream of 3D motion capture data. The gestural flow is modeled with a 2-level Hierarchical Hidden Markov Model (HHMM) where the sub-states correspond to gesture phases. The model is trained based on labels of complete gesture units and self-adaptive manipulators. The model is tested and validated on two datasets differing in genre and in method of capturing motion, and outperforms a state-of-the-art SVM classifier on a publicly available dataset.

In order to ensure smooth turn-taking between conversational participants, interlocutors must have ways of providing information to one another about whether they have finished speaking or intend to continue. The current work investigates Swedish speakers’ use of hand gestures in conjunction with turn change or turn hold in unrestricted, spontaneous speech. As has been reported by other researchers, we find that speakers’ gestures end before the end of speech in cases of turn change, while they may extend well beyond the end of a given speech chunk in the case of turn hold. We investigate the degree to which prosodic cues and gesture cues to turn transition in Swedish face-to-face conversation are complementary versus functioning additively. The co-occurrence of acoustic prosodic features and gesture at potential turn boundaries gives strong support for considering hand gestures as part of the prosodic system, particularly in the context of discourse-level information such as maintaining smooth turn transition.

This study explores the use of automatic methods to detect and extract handgesture movement co-occuring with speech. Two spontaneous dyadic dialogueswere analyzed using 3D motion-capture techniques to track hand movement.Automatic speech/non-speech detection was performed on the dialogues resultingin a series of connected talk spurts for each speaker. Temporal synchrony of onsetand offset of gesture and speech was studied between the automatic hand gesturetracking and talk spurts, and compared to an earlier study of head nods andsyllable synchronization. The results indicated onset synchronization between headnods and the syllable in the short temporal domain and between the onset of longergesture units and the talk spurt in a more extended temporal domain.

Motion capture of signs provides unique challenges in the field of multimodal data collection. The dense packaging of visual information requires high fidelity and high bandwidth of the captured data. Even though marker-based optical motion capture provides many desirable features such as high accuracy, global fitting, and the ability to record body and face simultaneously, it is not widely used to record finger motion, especially not for articulated and syntactic motion such as signs. Instead, most signing avatar projects use costly instrumented gloves, which require long calibration procedures. In this article, we evaluate the data quality obtained from optical motion capture of isolated signs from Swedish sign language with a large number of low-cost cameras. We also present a novel dual-sensor approach to combine the data with low-cost, five-sensor instrumented gloves to provide a recording method with low manual postprocessing. Finally, we evaluate the collected data and the dual-sensor approach as transferred to a highly stylized avatar. The application of the avatar is a game-based environment for training Key Word Signing (KWS) as augmented and alternative communication (AAC), intended for children with communication disabilities.

In this paper we study the production and perception of speech in diverse conditions for the purposes of accurate, flexible and highly intelligible talking face animation. We recorded audio, video and facial motion capture data of a talker uttering a,set of 180 short sentences, under three conditions: normal speech (in quiet), Lombard speech (in noise), and whispering. We then produced an animated 3D avatar with similar shape and appearance as the original talker and used an error minimization procedure to drive the animated version of the talker in a way that matched the original performance as closely as possible. In a perceptual intelligibility study with degraded audio we then compared the animated talker against the real talker and the audio alone, in terms of audio-visual word recognition rate across the three different production conditions. We found that the visual intelligibility of the animated talker was on par with the real talker for the Lombard and whisper conditions. In addition we created two incongruent conditions where normal speech audio was paired with animated Lombard speech or whispering. When compared to the congruent normal speech condition, Lombard animation yields a significant increase in intelligibility, despite the AV-incongruence. In a separate evaluation, we gathered subjective opinions on the different animations, and found that some degree of incongruence was generally accepted.

This paper reports on the extraction and analysis of head nods taken from motion capture data of spontaneous dialogue in Swedish. The head nods were extracted automatically and then manually classified in terms of gestures having a beat function or multifunctional gestures. Prosodic features were extracted from syllables co-occurring with the beat gestures. While the peak rotation of the nod is on average aligned with the stressed syllable, the results show considerable variation in fine temporal synchronization. The syllables co-occurring with the gestures generally show greater intensity, higher F0, and greater F0 range when compared to the mean across the entire dialogue. A functional analysis shows that the majority of the syllables belong to words bearing a focal accent.

This paper reports on a method developed for extracting and analyzing head gestures taken from motion capture data of spontaneous dialogue in Swedish. Candidate head gestures with beat function were extracted automatically and then manually classified using a 3D player which displays timesynced audio and 3D point data of the motion capture markers together with animated characters. Prosodic features were extracted from syllables co-occurring with a subset of the classified gestures. The beat gestures show considerable variation in temporal synchronization with the syllables, while the syllables generally show greater intensity, higher F0, and greater F0 range when compared to the mean across the entire dialogue. Additional features for further analysis and automatic classification of the head gestures are discussed.