Speech driven talking head from estimated articulatory features

In this paper, we present a talking head in which the lips and head motion are controlled using articulatory movements estimated from speech. A phone-size HMM-based inversion mapping is employed and trained in a semi-supervised fashion. The advantage of the use of articulatory features is that they can drive the lips motions and they have a close link with head movements. Speech inversion normally requires the training data recorded with electromagnetic articulograph (EMA), which restricts the naturalness of head movements. The present study considers a more realistic recording condition where the training data for the target speaker are recorded with a usual motion capture system rather than EMA. Different temporal clustering techniques are investigated for HMMbased mapping as well as a GMM-based frame-wise mapping as a baseline system. Objective and subjective experiments show that the synthesised motions are more natural using an HMM system than a GMM one, and estimated EMA features outperform prosodic features. Index Terms — inversion mapping, clustering, head motion synthesis 1

Articulatory features for speech-driven head motion synthesis

This study investigates the use of articulatory features for speech-driven head motion synthesis as opposed to prosody features such as F0 and energy that have been mainly used in the literature. In the proposed approach, multi-stream HMMs are trained jointly on the synchronous streams of speech and head motion data. Articulatory features can be regarded as an intermediate parametrisation of speech that are expected to have a close link with head movement. Measured head and articulatory movements acquired by EMA were synchronously recorded with speech. Measured articulatory data was compared to those predicted from speech using an HMM-based inversion mapping system trained in a semi-supervised fashion. Canonical correlation analysis (CCA) on a data set of free speech of 12 people shows that the articulatory features are more correlated with head rotation than prosodic and/or cepstral speech features. It is also shown that the synthesised head motion using articulatory features gave higher correlations with the original head motion than when only prosodic features are used. Index Terms: head motion synthesis, articulatory features, canonical correlation analysis, acoustic-to-articulatory mappin

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

3D visual speech animation using 2D videos

In visual speech animation, lip motion accuracy is of paramount importance for speech intelligibility, especially for the hard of hearing or foreign language learners. We present an approach for visual speech animation that uses tracked lip motion in front-view 2D videos of a real speaker to drive the lip motion of a synthetic 3D head. This makes use of a 3D morphable model (3DMM), built using 3D synthetic head poses, with corresponding landmarks identified in the 2D videos and the 3DMM. We show that using a wider range of synthetic head poses for different phoneme intensities to create a 3DMM, as well as a combination of front and side photographs of the real speakers rather than just front photographs to produce initial neutral 3D synthetic head poses, gives better animation results when compared to ground truth data consisting of front-view 2D videos of real speakers

Speech-driven head motion generation from waveforms

Head motion generation task for speech-driven virtual agent animation is commonly explored with handcrafted audio features, such as MFCCs as input features, plus additional features, such as energy and F0 in the literature. In this paper, we study the direct use of speech waveform to generate head motion. We claim that creating a task-specific feature from waveform to generate head motion leads to better performance than using standard acoustic features to generate head motion overall. At the same time, we completely abandon the handcrafted feature extraction process, leading to more effectiveness. However, the difficulty of creating a task-specific feature from waveform is their staggering quantity of irrelevant information, implicating potential cumbrance for neural network training. Thus, we apply a canonical-correlation-constrained autoencoder (CCCAE), where we are able to compress the high-dimensional waveform into a low-dimensional embedded feature, with the minimal error in reconstruction, and sustain the relevant information with the maximal cannonical correlation to head motion. We extend our previous research by including more speakers in our dataset and also adapt with a recurrent neural network, to show the feasibility of our proposed feature. Through comparisons between different acoustic features, our proposed feature, WavCCCAE, shows at least a 20% improvement in the correlation from the waveform, and outperforms the popular acoustic feature, MFCC, by at least 5% respectively for all speakers. Through the comparison in the feedforward neural network regression (FNN-regression) system, the WavCCCAE-based system shows comparable performance in objective evaluation. In long short-term memory (LSTM) experiments, LSTM-models improve the overall performance in normalised mean square error (NMSE) and CCA metrics, and adapt the WavCCCAEfeature better, which makes the proposed LSTM-regression system outperform the MFCC-based system. We also re-design the subjective evaluation, and the subjective results show the animations generated by models where WavCCCAEwas chosen to be better than the other models by the participants of MUSHRA test

Capture, Learning, and Synthesis of 3D Speaking Styles

Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input - even speech in languages other than English - and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201