Synthesising 3D facial motion from speech is a crucial problem manifesting in a multitude of applications such as computer games and movies. Recently proposed methods tackle this problem in controlled conditions of speech. In this paper, we introduce the first methodology for 3D facial motion synthesis from speech captured in arbitrary recording conditions ("in-the-wild") and independent of the speaker. For our purposes, we captured 4D sequences of people uttering 500 words, contained in the Lip Reading Words (LRW) a publicly available large-scale in-the-wild dataset, and built a set of 3D blendshapes appropriate for speech. We correlate the 3D shape parameters of the speech blendshapes to the LRW audio samples by means of a novel time-warping technique, named Deep Canonical Attentional Warping (DCAW), that can simultaneously learn hierarchical non-linear representations and a warping path in an end-to-end manner. We thoroughly evaluate our proposed methods, and show the ability of a deep learning model to synthesise 3D facial motion in handling different speakers and continuous speech signals in uncontrolled conditions.
. Language-based methods take advantage of the mapping between phonemes and their visual counterpart visemes. For example, @cite_34 proposed the JAw and LIp (JALI) model, a two-dimensional space that represents the jaw and lip movements of a facial animation based on psycholinguistic considerations. The main disadvantage of their study is the need for the speech signal, its text transcript and their alignment to create the facial animation. In another study, @cite_18 first proposed generating dynamic units for visual speech for realistic visual speech animation. In a more recent study, the authors @cite_27 initially transcribe the speech signal to phoneme labels, which are then fed to a deep fully connected network to predict person-specific shape and appearance parameters obtained by Active Appearance Model (AAM). A main limitation of this work is the need for speech to phoneme labels conversion.
@cite_27 @cite_18 @cite_34
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The rich signals we extract from facial expressions imposes high expectations for the science and art of facial animation. While the advent of high-resolution performance capture has greatly improved realism, the utility of procedural animation warrants a prominent place in facial animation workflow. We present a system that, given an input audio soundtrack and speech transcript, automatically generates expressive lip-synchronized facial animation that is amenable to further artistic refinement, and that is comparable with both performance capture and professional animator output. Because of the diversity of ways we produce sound, the mapping from phonemes to visual depictions as visemes is many-valued. We draw from psycholinguistics to capture this variation using two visually distinct anatomical actions: Jaw and Lip, wheresound is primarily controlled by jaw articulation and lower-face muscles, respectively. We describe the construction of a transferable template jali 3D facial rig, built upon the popular facial muscle action unit representation facs. We show that acoustic properties in a speech signal map naturally to the dynamic degree of jaw and lip in visual speech. We provide an array of compelling animation clips, compare against performance capture and existing procedural animation, and report on a brief user study. This paper presents an articulatory modelling approach to convert acoustic speech into realistic mouth animation. We directly model the movements of articulators, such as lips, tongue, and teeth, using a dynamic Bayesian network (DBN)-based audio-visual articulatory model (AVAM). A multiple-stream structure with a shared articulator layer is adopted in the model to synchronously associate the two building blocks of speech, i.e., audio and video. This model not only describes the synchronization between visual articulatory movements and audio speech, but also reflects the linguistic fact that different articulators evolve asynchronously. We also present a Baum-Welch DBN inversion (DBNI) algorithm to generate optimal facial parameters from audio given the trained AVAM under maximum likelihood (ML) criterion. Extensive objective and subjective evaluations on the JEWEL audio-visual dataset demonstrate that compared with phonemic HMM approaches, facial parameters estimated by our approach follow the true parameters more accurately, and the synthesized facial animation sequences are so lively that 38 of them are undistinguishable This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations. We present a new method for generating a dynamic, concatenative, unit of visual speech that can generate realistic visual speech animation. We redefine visemes as temporal units that describe distinctive speech movements of the visual speech articulators. Traditionally visemes have been surmized as the set of static mouth shapes representing clusters of contrastive phonemes (e.g. p, b, m , and f, v ). In this work, the motion of the visual speech articulators are used to generate discrete, dynamic visual speech gestures. These gestures are clustered, providing a finite set of movements that describe visual speech, the visemes. Dynamic visemes are applied to speech animation by simply concatenating viseme units. We compare to static visemes using subjective evaluation. We find that dynamic visemes are able to produce more accurate and visually pleasing speech animation given phonetically annotated audio, reducing the amount of time that an animator needs to spend manually refining the animation. We propose a three-stage pixel-based visual front end for automatic speechreading (lipreading) that results in significantly improved recognition performance of spoken words or phonemes. The proposed algorithm is a cascade of three transforms applied on a three-dimensional video region-of-interest that contains the speaker's mouth area. The first stage is a typical image compression transform that achieves a high-energy, reduced-dimensionality representation of the video data. The second stage is a linear discriminant analysis-based data projection, which is applied on a concatenation of a small amount of consecutive image transformed video data. The third stage is a data rotation by means of a maximum likelihood linear transform that optimizes the likelihood of the observed data under the assumption of their class-conditional multivariate normal distribution with diagonal covariance. We applied the algorithm to visual-only 52-class phonetic and 27-class visemic classification on a 162-subject, 8-hour long, large vocabulary, continuous speech audio-visual database. We demonstrated significant classification accuracy gains by each added stage of the proposed algorithm which, when combined, can achieve up to 27 improvement. Overall, we achieved a 60 (49 ) visual-only frame-level visemic classification accuracy with (without) use of test set viseme boundaries. In addition, we report improved audio-visual phonetic classification over the use of a single-stage image transform visual front end. Finally, we discuss preliminary speech recognition results. Conversion of silent articulation captured by ultrasound and video to modal speech.Comparison of GMM and full-covariance phonetic HMM without vocabulary limitation.HMM-based approach allows the use of linguistic information for regularization.Objective evaluation showed a lower but more fluctuant spectral distortion for HMM.Perceptual evaluation showed a better intelligibility for HMM on consonants. This article investigates the use of statistical mapping techniques for the conversion of articulatory movements into audible speech with no restriction on the vocabulary, in the context of a silent speech interface driven by ultrasound and video imaging. As a baseline, we first evaluated the GMM-based mapping considering dynamic features, proposed by (2007) for voice conversion. Then, we proposed a 'phonetically-informed' version of this technique, based on full-covariance HMM. This approach aims (1) at modeling explicitly the articulatory timing for each phonetic class, and (2) at exploiting linguistic knowledge to regularize the problem of silent speech conversion. Both techniques were compared on continuous speech, for two French speakers (one male, one female). For modal speech, the HMM-based technique showed a lower spectral distortion (objective evaluation). However, perceptual tests (transcription and XAB discrimination tests) showed a better intelligibility of the GMM-based technique, probably related to its less fluctuant quality. For silent speech, a perceptual identification test revealed a better segmental intelligibility for the HMM-based technique on consonants. In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-realistic talking head synthesis. An audio-visual database of a person is recorded first for training a statistical Hidden Markov Model (HMM) of Lips movement. The HMM is then used to generate the dynamic trajectory of lips movement for given speech signals in the maximum probability sense. The generated trajectory is then used as a guide to select, from the original training database, an optimal sequence of lips images which are then stitched back to a background head video. We also propose a minimum generation error (MGE) training method to refine the audio-visual HMM to improve visual speech trajectory synthesis. Compared with the traditional maximum likelihood (ML) estimation, the proposed MGE training explicitly optimizes the quality of generated visual speech trajectory, where the audio-visual HMM modeling is jointly refined by using a heuristic method to find the optimal state alignment and a probabilistic descent algorithm to optimize the model parameters under the MGE criterion. In objective evaluation, compared with the ML-based method, the proposed MGE-based method achieves consistent improvement in the mean square error reduction, correlation increase, and recovery of global variance. For as short as 20 min recording of audio video footage, the proposed system can synthesize a highly photo-realistic talking head in sync with the given speech signals (natural or TTS synthesized). This system won the first place in the A V consistency contest in LIPS Challenge, perceptually evaluated by recruited human subjects. We propose a coupled hidden Markov model (CHMM) approach to video-realistic speech animation, which realizes realistic facial animations driven by speaker independent continuous speech. Different from hidden Markov model (HMM)-based animation approaches that use a single-state chain, we use CHMMs to explicitly model the subtle characteristics of audio-visual speech, e.g., the asynchrony, temporal dependency (synchrony), and different speech classes between the two modalities. We derive an expectation maximization (EM)-based A V conversion algorithm for the CHMMs, which converts acoustic speech into decent facial animation parameters. We also present a video-realistic speech animation system. The system transforms the facial animation parameters to a mouth animation sequence, refines the animation with a performance refinement process, and finally stitches the animated mouth with a background facial sequence seamlessly. We have compared the animation performance of the CHMM with the HMMs, the multi-stream HMMs and the factorial HMMs both objectively and subjectively. Results show that the CHMMs achieve superior animation performance. The ph-vi-CHMM system, which adopts different state variables (phoneme states and viseme states) in the audio and visual modalities, performs the best. The proposed approach indicates that explicitly modelling audio-visual speech is promising for speech animation. The verbal and nonverbal channels of human communication are internally and intricately connected. As a result, gestures and speech present high levels of correlation and coordination. This relationship is greatly affected by the linguistic and emotional content of the message. The present paper investigates the influence of articulation and emotions on the interrelation between facial gestures and speech. The analyses are based on an audio-visual database recorded from an actress with markers attached to her face, who was asked to read semantically neutral sentences, expressing four emotion states (neutral, sadness, happiness, and anger). A multilinear regression framework is used to estimate facial features from acoustic speech parameters. The levels of coupling between the communication channels are quantified by using Pearson's correlation between the recorded and estimated facial features. The results show that facial and acoustic features are strongly interrelated, showing levels of correlation higher than r = 0.8 when the mapping is computed at sentence-level using spectral envelope speech features. The results reveal that the lower face region provides the highest activeness and correlation levels. Furthermore, the correlation levels present significant interemo- tional differences, which suggest that emotional content affect the relationship between facial gestures and speech. Principal component analysis (PCA) shows that the audiovisual mapping parameters are grouped in a smaller subspace, which suggests that there is an emotion-dependent structure that is preserved from across sentences. The results suggest that this internal structure seems to be easy to model when prosodic-features are used to estimate the audiovisual mapping. The results also reveal that the correlation levels within a sentence vary according to broad phonetic properties presented in the sentence. Consonants, especially unvoiced and fricative sounds, present the lowest correlation levels. Likewise, the results show that facial gestures are linked at different resolutions. While the orofacial area is locally connected with the speech, other facial gestures such as eyebrow motion are linked only at the sentence-level. The results presented here have important implications for applications such as facial animation and multimodal emotion recognition. Recent improvements in tracking and feature extraction mean that speaker-dependent lip-reading of continuous speech using a medium size vocabulary (around 1000 words) is realistic. However, the recognition of previously unseen speakers has been found to be a very challenging task, because of the large variation in lip-shapes across speakers and the lack of large, tracked databases of visual features, which are very expensive to produce. By adapting a technique that is established in speech recognition but has not previously been used in lip-reading, we show that error-rates for speaker-independent lip-reading can be very significantly reduced. Furthermore, we show that error-rates can be even further reduced by the additional use of Deep Neural Networks (DNN). We also find that there is no need to map phonemes to visemes for context-dependent visual speech transcription. In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-real talking head synthesis. It renders a smooth and natural video of articulators in sync with given speech signals. An audio-visual database is used to train a statistical Hidden Markov Model (HMM) of lips movement first and the trained model is then used to generate a visual parameter trajectory of lips movement for given speech signals, all in the maximum likelihood sense. The HMM generated trajectory is then used as a guide to select, in the original training database, an optimal sequence of mouth images which are then stitched back to a background head video. The whole procedure is fully automatic and data driven. With an audio video footage as short as 20 minutes from a speaker, the proposed system can synthesize a highly photo-real video in sync with the given speech signals. This system won the FIRST place in the Audio-Visual match contest in LIPS2009 Challenge, which was perceptually evaluated by recruited human subjects. Index Terms: visual speech synthesis, photo-real, talking head, trajectory-guided Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art. The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information. We integrate speech cues from many sources and this improves intelligibility, especially when the acoustic signal is degraded. The paper shows how this additional, often complementary, visual speech information can be used for speech recognition. Three methods for parameterizing lip image sequences for recognition using hidden Markov models are compared. Two of these are top-down approaches that fit a model of the inner and outer lip contours and derive lipreading features from a principal component analysis of shape or shape and appearance, respectively. The third, bottom-up, method uses a nonlinear scale-space analysis to form features directly from the pixel intensity. All methods are compared on a multitalker visual speech recognition task of isolated letters. In this paper we present a new method for mapping a natural speech to the lip shape animation in the real time. The speech signal, represented by MFCC vectors, is classified into viseme classes using neural networks. The topology of neural networks is automatically configured using genetic algorithms. This eliminates the need for tedious manual neural network design by trial and error and considerably improves the viseme classification results. This method is suitable for real-time and offline applications We propose a new photo-realistic, voice driven only (i.e. no linguistic info of the voice input is needed) talking head. The core of the new talking head is a context-dependent, multilayer, Deep Neural Network (DNN), which is discriminatively trained over hundreds of hours, speaker independent speech data. The trained DNN is then used to map acoustic speech input to 9,000 tied “senone” states probabilistically. For each photo-realistic talking head, an HMM-based lips motion synthesizer is trained over the speaker’s audio visual training data where states are statistically mapped to the corresponding lips images. In test, for given speech input, DNN predicts the likely states in their posterior probabilities and photo-realistic lips animation is then rendered through the DNN predicted state lattice. The DNN trained on English, speaker independent data has also been tested with other language input, e.g. Mandarin, Spanish, etc. to mimic the lips movements cross-lingually. Subjective experiments show that lip motions thus rendered for 15 non-English languages are highly synchronized with the audio input and photo-realistic to human eyes perceptually. This paper describes our initial work in developing a real-time audio-visual Chinese speech synthesizer with a 3D expressive avatar. The avatar model is parameterized according to the MPEG-4 facial animation standard [1]. This standard offers a compact set of facial animation parameters (FAPs) and feature points (FPs) to enable realization of 20 Chinese visemes and 7 facial expressions (i.e. 27 target facial configurations). The Xface [2] open source toolkit enables us to define the influence zone for each FP and the deformation function that relates them. Hence we can easily animate a large number of coordinates in the 3D model by specifying values for a small set of FAPs and their FPs. FAP values for 27 target facial configurations were estimated from available corpora. We extended the dominance blending approach to effect animations for coarticulated visemes superposed with expression changes. We selected six sentiment-carrying text messages and synthesized expressive visual speech (for all expressions, in randomized order) with neutral audio speech. A perceptual experiment involving 11 subjects shows that they can identify the facial expression that matches the text message’s sentiment 85 of the time. Display Omitted This paper presents a phonetic and visemic information-based audio-visual speech recognizer (AVSR). Active appearance model (AAM) is used to extract the visual features as it finely represents the shape and appearance information extracted from jaw and lip region. Consideration of visual features along with traditional acoustic feature has been found to be promising in the complex auditory environment. However, most of the existing AVSR systems rarely faced the visual domain problems. In this work, a real world multiple camera corpus audio visual in car (AVICAR) is used for the speech recognition experiment. Texas Instruments and Massachusetts Institute of Technology (TIMIT) corpus sentence portion is used to study the performance of bimodal audio-visual speech recognizer. To consider "Mc-Guruk" effect, acoustic and visual models are trained according to phonetic and visemic information, respectively. Phonetic-visemic AVSR system shows significant improvement over phonetic AVSR system. We present a deep learning framework for real-time speech-driven 3D facial animation from just raw waveforms. Our deep neural network directly maps an input sequence of speech audio to a series of micro facial action unit activations and head rotations to drive a 3D blendshape face model. In particular, our deep model is able to learn the latent representations of time-varying contextual information and affective states within the speech. Hence, our model not only activates appropriate facial action units at inference to depict different utterance generating actions, in the form of lip movements, but also, without any assumption, automatically estimates emotional intensity of the speaker and reproduces her ever-changing affective states by adjusting strength of facial unit activations. For example, in a happy speech, the mouth opens wider than normal, while other facial units are relaxed; or in a surprised state, both eyebrows raise higher. Experiments on a diverse audiovisual corpus of different actors across a wide range of emotional states show interesting and promising results of our approach. Being speaker-independent, our generalized model is readily applicable to various tasks in human-machine interaction and animation. In this paper, we propose a complete pipeline of efficient and low-cost techniques to construct a realistic 3D text-driven emotive audio-visual avatar from a single 2D frontal-view face image of any person on the fly. This real-time conversion is achieved through three steps. First, a personalized 3D face model is built based on the 2D face image using a fully automatic 3D face shape and texture reconstruction framework. Second, using standard MPEG-4 FAPs (Facial Animation Parameters), the face model is animated by the Viseme and expression channels and is complemented by the visual prosody channel that controls head, eye and eyelid movements. Finally, the facial animation is combined and synchronized with the emotive synthetic speech generated by incorporating an emotion transformer into a Festival-MBROLA text to neutral speech synthesizer. Abstract This paper examines the degrees of correlation among vocal-tract and facial movement data and the speech acoustics. Multilinear techniques are applied to support the claims that facial motion during speech is largely a by-product of producing the speech acoustics and further that the spectral envelope of the speech acoustics can be better estimated by the 3D motion of the face than by the midsagittal motion of the anterior vocal-tract (lips, tongue and jaw). Experimental data include measurements of the motion of markers placed on the face and in the vocal-tract, as well as the speech acoustics, for two subjects. The numerical results obtained show that, for both subjects, 91 of the total variance observed in the facial motion data could be determined from vocal-tract motion by means of simple linear estimators. For the inverse path, i.e. recovery of vocal-tract motion from facial motion, the results indicate that about 80 of the variance observed in the vocal-tract can be estimated from the face. Regarding the speech acoustics, it is observed that, in spite of the nonlinear relation between vocal-tract geometry and acoustics, linear estimators are sufficient to determine between 72 and 85 (depending on subject and utterance) of the variance observed in the RMS amplitude and LSP parametric representation of the spectral envelope. A dimensionality analysis is also carried out, and shows that between four and eight components are sufficient to represent the mappings examined. Finally, it is shown that even the tongue, which is an articulator not necessarily coupled with the face, can be recovered reasonably well from facial motion since it frequently displays the same kind of temporal pattern as the jaw during speech.