Abstract In this article, we present a live speech-driven, avatar-mediated, three-party telepresence system, through which three distant users, embodied as avatars in a shared 3D virtual world, can perform natural three-party telepresence that does not require tracking devices. Based on live speech input from three users, this system can real-time generate the corresponding conversational motions of all the avatars, including head motion, eye motion, lip movement, torso motion, and hand gesture. All motions are generated automatically at each user side based on live speech input, and a cloud server is utilized to transmit and synchronize motion and speech among different users. We conduct a formal user study to evaluate the usability and effectiveness of the system by comparing it with a well-known online virtual world, Second Life, and a widely-used online teleconferencing system, Skype. The user study results indicate our system can provide a measurably better telepresence user experience than the two widely-used methods.
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S2M-Net: Speech Driven Three-party Conversational Motion Synthesis Networks
In this paper we propose a novel conditional generative adversarial network (cGAN) architecture, called S2M-Net, to holistically synthesize realistic three-party conversational animations based on acoustic speech input together with speaker marking (i.e., the speak- ing time of each interlocutor). Specifically, based on a pre-collected three-party conversational motion dataset, we design and train the S2M-Net for three-party conversational animation synthesis. In the architecture, a generator contains a LSTM encoder to encode a sequence of acoustic speech features to a latent vector that is further fed into a transform unit to transform the latent vector into a gesture kinematics space. Then, the output of this transform unit is fed into a LSTM decoder to generate corresponding three-party conversational gesture kinematics. Meanwhile, a discriminator is implemented to check whether an input sequence of three-party conversational gesture kinematics is real or fake. To evaluate our method, besides quantitative and qualitative evaluations, we also conducted paired comparison user studies to compare it with the state of the art.
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- Award ID(s):
- 2005430
- PAR ID:
- 10463603
- Date Published:
- Journal Name:
- Proceeding of ACM SIGGRAPH Conference on Motion, Interaction, and Games 2022
- Page Range / eLocation ID:
- 2:1 to 2:10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co‐speech gestures is a long‐standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non‐periodic nature of human co‐speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep‐learning‐based generative models that benefit from the growing availability of data. This review article summarizes co‐speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule‐based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non‐linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human‐like motion; grounding the gesture in the co‐occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.
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ISCA (Ed.)In this paper, we present MixRep, a simple and effective data augmentation strategy based on mixup for low-resource ASR. MixRep interpolates the feature dimensions of hidden representations in the neural network that can be applied to both the acoustic feature input and the output of each layer, which generalizes the previous MixSpeech method. Further, we propose to combine the mixup with a regularization along the time axis of the input, which is shown as complementary. We apply MixRep to a Conformer encoder of an E2E LAS architecture trained with a joint CTC loss. We experiment on the WSJ dataset and subsets of the SWB dataset, covering reading and telephony conversational speech. Experimental results show that MixRep consistently outperforms other regularization methods for low-resource ASR. Compared to a strong SpecAugment baseline, MixRep achieves a +6.5% and a +6.7% relative WER reduction on the eval92 set and the Callhome part of the eval'2000 set.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3561975.3562954
