Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

TEXT-DRIVEN MOUTH ANIMATION FOR HUMAN COMPUTER INTERACTION WITH PERSONAL ASSISTANT

International audiencePersonal assistants are becoming more pervasive in our environments but still do not provide natural interactions. Their lack of realism in term of expressiveness and their lack of visual feedback can create frustrating experiences and make users lose patience. In this sense, we propose an end-to-end trainable neural architecture for text-driven 3D mouth animations. Previous works showed such architectures provide better realism and could open the door for integrated affective Human Computer Interface (HCI). Our study shows that such visual feedback improves users' comfort for 78% of the candidates significantly while slightly improving their time perception

Breathing Life into Faces: Speech-driven 3D Facial Animation with Natural Head Pose and Detailed Shape

The creation of lifelike speech-driven 3D facial animation requires a natural and precise synchronization between audio input and facial expressions. However, existing works still fail to render shapes with flexible head poses and natural facial details (e.g., wrinkles). This limitation is mainly due to two aspects: 1) Collecting training set with detailed 3D facial shapes is highly expensive. This scarcity of detailed shape annotations hinders the training of models with expressive facial animation. 2) Compared to mouth movement, the head pose is much less correlated to speech content. Consequently, concurrent modeling of both mouth movement and head pose yields the lack of facial movement controllability. To address these challenges, we introduce VividTalker, a new framework designed to facilitate speech-driven 3D facial animation characterized by flexible head pose and natural facial details. Specifically, we explicitly disentangle facial animation into head pose and mouth movement and encode them separately into discrete latent spaces. Then, these attributes are generated through an autoregressive process leveraging a window-based Transformer architecture. To augment the richness of 3D facial animation, we construct a new 3D dataset with detailed shapes and learn to synthesize facial details in line with speech content. Extensive quantitative and qualitative experiments demonstrate that VividTalker outperforms state-of-the-art methods, resulting in vivid and realistic speech-driven 3D facial animation

Modelling talking human faces

This thesis investigates a number of new approaches for visual speech synthesis using data-driven methods to implement a talking face. The main contributions in this thesis are the following. The accuracy of shared Gaussian process latent variable model (SGPLVM) built using the active appearance model (AAM) and relative spectral transform-perceptual linear prediction (RASTAPLP) features is improved by employing a more accurate AAM. This is the first study to report that using a more accurate AAM improves the accuracy of SGPLVM. Objective evaluation via reconstruction error is performed to compare the proposed approach against previously existing methods. In addition, it is shown experimentally that the accuracy of AAM can be improved by using a larger number of landmarks and/or larger number of samples in the training data. The second research contribution is a new method for visual speech synthesis utilising a fully Bayesian method namely the manifold relevance determination (MRD) for modelling dynamical systems through probabilistic non-linear dimensionality reduction. This is the first time MRD was used in the context of generating talking faces from the input speech signal. The expressive power of this model is in the ability to consider non-linear mappings between audio and visual features within a Bayesian approach. An efficient latent space has been learnt iii Abstract iv using a fully Bayesian latent representation relying on conditional nonlinear independence framework. In the SGPLVM the structure of the latent space cannot be automatically estimated because of using a maximum likelihood formulation. In contrast to SGPLVM the Bayesian approaches allow the automatic determination of the dimensionality of the latent spaces. The proposed method compares favourably against several other state-of-the-art methods for visual speech generation, which is shown in quantitative and qualitative evaluation on two different datasets. Finally, the possibility of incremental learning of AAM for inclusion in the proposed MRD approach for visual speech generation is investigated. The quantitative results demonstrate that using MRD in conjunction with incremental AAMs produces only slightly less accurate results than using batch methods. These results support a way of training this kind of models on computers with limited resources, for example in mobile computing. Overall, this thesis proposes several improvements to the current state-of-the-art in generating talking faces from speech signal leading to perceptually more convincing results

A Review of Verbal and Non-Verbal Human-Robot Interactive Communication

In this paper, an overview of human-robot interactive communication is presented, covering verbal as well as non-verbal aspects of human-robot interaction. Following a historical introduction, and motivation towards fluid human-robot communication, ten desiderata are proposed, which provide an organizational axis both of recent as well as of future research on human-robot communication. Then, the ten desiderata are examined in detail, culminating to a unifying discussion, and a forward-looking conclusion

AudioViewer: Learning to Visualize Sounds

A long-standing goal in the field of sensory substitution is to enable sound perception for deaf and hard of hearing (DHH) people by visualizing audio content. Different from existing models that translate to hand sign language, between speech and text, or text and images, we target immediate and low-level audio to video translation that applies to generic environment sounds as well as human speech. Since such a substitution is artificial, without labels for supervised learning, our core contribution is to build a mapping from audio to video that learns from unpaired examples via high-level constraints. For speech, we additionally disentangle content from style, such as gender and dialect. Qualitative and quantitative results, including a human study, demonstrate that our unpaired translation approach maintains important audio features in the generated video and that videos of faces and numbers are well suited for visualizing high-dimensional audio features that can be parsed by humans to match and distinguish between sounds and words. Code and models are available at https://chunjinsong.github.io/audioviewe

Development of the huggable social robot Probo: on the conceptual design and software architecture

This dissertation presents the development of a huggable social robot named Probo. Probo embodies a stuffed imaginary animal, providing a soft touch and a huggable appearance. Probo's purpose is to serve as a multidisciplinary research platform for human-robot interaction focused on children. In terms of a social robot, Probo is classified as a social interface supporting non-verbal communication. Probo's social skills are thereby limited to a reactive level. To close the gap with higher levels of interaction, an innovative system for shared control with a human operator is introduced. The software architecture de nes a modular structure to incorporate all systems into a single control center. This control center is accompanied with a 3D virtual model of Probo, simulating all motions of the robot and providing a visual feedback to the operator. Additionally, the model allows us to advance on user-testing and evaluation of newly designed systems. The robot reacts on basic input stimuli that it perceives during interaction. The input stimuli, that can be referred to as low-level perceptions, are derived from vision analysis, audio analysis, touch analysis and object identification. The stimuli will influence the attention and homeostatic system, used to de ne the robot's point of attention, current emotional state and corresponding facial expression. The recognition of these facial expressions has been evaluated in various user-studies. To evaluate the collaboration of the software components, a social interactive game for children, Probogotchi, has been developed. To facilitate interaction with children, Probo has an identity and corresponding history. Safety is ensured through Probo's soft embodiment and intrinsic safe actuation systems. To convey the illusion of life in a robotic creature, tools for the creation and management of motion sequences are put into the hands of the operator. All motions generated from operator triggered systems are combined with the motions originating from the autonomous reactive systems. The resulting motion is subsequently smoothened and transmitted to the actuation systems. With future applications to come, Probo is an ideal platform to create a friendly companion for hospitalised children

Discovering Dynamic Visemes

Abstract This thesis introduces a set of new, dynamic units of visual speech which are learnt using computer vision and machine learning techniques. Rather than clustering phoneme labels as is done traditionally, the visible articulators of a speaker are tracked and automatically segmented into short, visually intuitive speech gestures based on the dynamics of the articulators. The segmented gestures are clustered into dynamic visemes, such that movements relating to the same visual function appear within the same cluster. Speech animation can then be generated on any facial model by mapping a phoneme sequence to a sequence of dynamic visemes, and stitching together an example of each viseme in the sequence. Dynamic visemes model coarticulation and maintain the dynamics of the original speech, so simple blending at the concatenation boundaries ensures a smooth transition. The efficacy of dynamic visemes for computer animation is formally evaluated both objectively and subjectively, and compared with traditional phoneme to static lip-pose interpolation