Audiovisual speech synthesis involves synthesizing a talking face while maximizing the coherency of the acoustic and visual speech. To solve this problem, we propose using AVTacotron2, which is an end-to-end text-to-audiovisual speech synthesizer based on the Tacotron2 architecture. AVTacotron2 converts a sequence of phonemes into a sequence of acoustic features and the corresponding controllers of a face model. The output acoustic features are passed through a WaveRNN model to reconstruct the speech waveform. The speech waveform and the output facial controllers are used to generate the corresponding video of the talking face. As a baseline, we use a modular system, where acoustic speech is synthesized from text using the traditional Tacotron2. The reconstructed acoustic speech is then used to drive the controls of the face model using an independently trained audio-to-facial-animation neural network. We further condition both the end-to-end and modular approaches on emotion embeddings that encode the required prosody to generate emotional audiovisual speech. A comprehensive analysis shows that the end-to-end system is able to synthesize close to human-like audiovisual speech with mean opinion scores (MOS) of 4.1, which is the same MOS obtained on the ground truth generated from professionally recorded videos.

Speech-driven visual speech synthesis involves mapping acoustic speech features to the corresponding lip animation controls for a face model. This mapping can take many forms, but a powerful approach is to use deep neural networks (DNNs). The lack of synchronized audio, video, and depth data is a limitation to reliably train DNNs, especially for speaker-independent models. In this paper, we investigate adapting an automatic speech recognition (ASR) acoustic model (AM) for the visual speech synthesis problem. We train the ASR-AM on ten thousand hours of audio-only transcribed speech. The ASR-AM is then adapted to the visual speech synthesis domain using ninety hours of synchronized audio-visual speech. Using a subjective assessment test, we compared the performance of the AM-initialized DNN to a randomly initialized model. The results show that viewers significantly prefer animations generated from the AM-initialized DNN than the ones generated using the randomly initialized model. We conclude that visual speech synthesis can significantly benefit from the powerful representation of speech in the ASR acoustic models.

Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild [20] database where close-to-human accuracy is achieved while processing images in real-time.

Early works on human action recognition focused on tracking and classifying articulated body motions. Such methods required accurate localisation of body parts, which is a difficult task, particularly under realistic imaging conditions. As such, recent trends have shifted towards the use of more abstract, low-level appearance features such as spatio-temporal interest points. Motivated by the recent progress in pose estimation, we feel that pose-based action recognition systems warrant a second look. In this paper, we address the question of whether pose estimation is useful for action recognition or if it is better to train a classifier only on low-level appearance features drawn from video data. We compare pose-based, appearance-based and combined pose and appearance features for action recognition in a home-monitoring scenario. Our experiments show that pose-based features outperform low-level appearance features, even when heavily corrupted by noise, suggesting that pose estimation is beneficial for the action recognition task. © 2011. The copyright of this document resides with its authors.

Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the ever more affordable 3D sensing technologies available today. To achieve robustness, we formulate pose estimation as a regression problem. While detecting specific face parts like the nose is sensitive to occlusions, learning the regression on rather generic surface patches requires enormous amount of training data in order to achieve accurate estimates. We propose to use random regression forests for the task at hand, given their capability to handle large training datasets. Moreover, we synthesize a great amount of annotated training data using a statistical model of the human face. In our experiments, we show that our approach can handle real data presenting large pose changes, partial occlusions, and facial expressions, even though it is trained only on synthetic neutral face data. We have thoroughly evaluated our system on a publicly available database on which we achieve state-of-the-art performance without having to resort to the graphics card.

Communication between humans deeply relies on the capability of expressing and recognizing feelings. For this reason, research on human-machine interaction needs to focus on the recognition and simulation of emotional states, prerequisite of which is the collection of affective corpora. Currently available datasets still represent a bottleneck for the difficulties arising during the acquisition and labeling of affective data. In this work, we present a new audio-visual corpus for possibly the two most important modalities used by humans to communicate their emotional states, namely speech and facial expression in the form of dense dynamic 3-D face geometries. We acquire high-quality data by working in a controlled environment and resort to video clips to induce affective states. The annotation of the speech signal includes: transcription of the corpus text into the phonological representation, accurate phone segmentation, fundamental frequency extraction, and signal intensity estimation of the speech signals. We employ a real-time 3-D scanner to acquire dense dynamic facial geometries and track the faces throughout the sequences, achieving full spatial and temporal correspondences. The corpus is a valuable tool for applications like affective visual speech synthesis or view-independent facial expression recognition.

We present a novel method for mouth localization in the context of multimodal speech recognition where audio and visual cues are fused to improve the speech recognition accuracy. While facial feature points like mouth corners or lip contours are commonly used to estimate at least scale, position, and orientation of the mouth, we propose a Hough transform-based method. Instead of relying on a predefined sparse subset of mouth features, it casts probabilistic votes for the mouth center from several patches in the neighborhood and accumulates the votes in a Hough image. This makes the localization more robust as it does not rely on the detection of a single feature. In addition, we exploit the different shape properties of eyes and mouth in order to localize the mouth more efficiently. Using the rotation invariant representation of the iris, scale and orientation can be efficiently inferred from the localized eye positions. The superior accuracy of our method and quantitative improvements for audio-visual speech recognition over monomodal approaches are demonstrated on two datasets. © 2009. The copyright of this document resides with its authors.