GPU-accelerated lip-tracking library

A major part of having correct pronunciation when learning a new language is moving your lips in the correct way. This is a difficult thing to learn and to teach. One solution to this is software which tracks a student\u27s lip movements and provides feedback. This paper describes how we have created a C++ library to accurately track lips in provided images. Further, this library attempts to use a CUDA-enabled GPU implementation to improve the algorithm\u27s performance. It will fall back on a CPU implementation if such a GPU is not found. As a result, the lip tracking library runs on Windows, Linux, and OS X, as well as Android devices

A new visual speech modelling approach for visual speech recognition

In this paper we propose a new learning-based representation that is referred to as Visual Speech Unit (VSU) for visual speech recognition (VSR). The new Visual Speech Unit concept proposes an extension of the standard viseme model that is currently applied for VSR by including in this representation not only the data associated with the visemes, but also the transitory information between consecutive visemes. The developed speech recognition system consists of several computational stages: (a) lips segmentation, (b) construction of the Expectation-Maximization Principal Component Analysis (EM-PCA) manifolds from the input video image, (c) registration between the models of the VSUs and the EM-PCA data constructed from the input image sequence and (d) recognition of the VSUs using a standard Hidden Markov Model (HMM) classification scheme. In this paper we were particularly interested to evaluate the classification accuracy obtained for our new VSU models when compared with that attained for standard (MPEG-4) viseme models. The experimental results indicate that we achieved 90% recognition rate when the system has been applied to the identification of 60 classes of VSUs, while the recognition rate for the standard set of MPEG-4 visemes was only 52%

Extraction automatique de contour de lèvre à partir du modèle CLNF

International audienceDans cet article nous proposons une nouvelle solution pour extraire le contour interne des lèvres d'un locuteur sans utiliser d'artifices. La méthode s'appuie sur un algorithme récent d'extraction du contour de visage développé en vision par ordinateur, CLNF pour Constrained Local Neural Field. Cet algorithme fournit en particulier 8 points caractéristiques délimitant le contour interne des lèvres. Appliqué directement à nos données audio-visuelles du locuteur, le CLNF donne de très bons résultats dans environ 70% des cas. Des erreurs subsistent cependant pour le reste des cas. Nous proposons des solutions pour estimer un contour raisonnable des lèvres à partir des points fournis par CLNF utilisant l'interpolation par spline permettant de corriger ses erreurs et d'extraire correctement les paramètres labiaux classiques. Les évaluations sur une base de données de 179 images confirment les performances de notre algorithme. ABSTRACT Automatic lip contour extraction using CLNF model. In this paper a new approach to extract the inner contour of the lips of a speaker without using artifices is proposed. The method is based on a recent face contour extraction algorithm developed in computer vision. This algorithm, which is called Constrained Local Neural Field (CLNF), provides 8 characteristic points (landmarks) defining the inner contour of the lips. Applied directly to our audiovisual data of the speaker, CLNF gives very satisfactory results in about 70% of cases. However, errors exist for the remaining cases. We offer solutions for estimating a reasonable inner lip contour from the landmarks provided by CLNF based on spline to correct its bad behaviors and to extract the suitable labial parameters A, B and S. The evaluations on a 179 image database confirm performance of our algorithm. MOTS-CLES : modèle CLNF, spline, contour des lèvres, paramètres labiaux, parole visuelle

A New Re-synchronization Method based Multi-modal Fusion for Automatic Continuous Cued Speech Recognition

Cued Speech (CS) is an augmented lip reading complemented by hand coding, and it is very helpful to the deaf people. Automatic CS recognition can help communications between the deaf people and others. Due to the asynchronous nature of lips and hand movements, fusion of them in automatic CS recognition is a challenging problem. In this work, we propose a novel re-synchronization procedure for multi-modal fusion, which aligns the hand features with lips feature. It is realized by delaying hand position and hand shape with their optimal hand preceding time which is derived by investigating the temporal organizations of hand position and hand shape movements in CS. This re-synchronization procedure is incorporated into a practical continuous CS recognition system that combines convolutional neural network (CNN) with multi-stream hidden markov model (MSHMM). A significant improvement of about 4.6% has been achieved retaining 76.6% CS phoneme recognition correctness compared with the state-of-the-art architecture (72.04%), which did not take into account the asynchrony issue of multi-modal fusion in CS. To our knowledge, this is the first work to tackle the asynchronous multi-modal fusion in the automatic continuous CS recognition

Robust visual speech recognition using optical flow analysis and rotation invariant features

The focus of this thesis is to develop computer vision algorithms for visual speech recognition system to identify the visemes. The majority of existing speech recognition systems is based on audio-visual signals and has been developed for speech enhancement and is prone to acoustic noise. Considering this problem, aim of this research is to investigate and develop a visual only speech recognition system which should be suitable for noisy environments. Potential applications of such a system include the lip-reading mobile phones, human computer interface (HCI) for mobility-impaired users, robotics, surveillance, improvement of speech based computer control in a noisy environment and for the rehabilitation of the persons who have undergone a laryngectomy surgery. In the literature, there are several models and algorithms available for visual feature extraction. These features are extracted from static mouth images and characterized as appearance and shape based features. However, these methods rarely incorporate the time dependent information of mouth dynamics. This dissertation presents two optical flow based approaches of visual feature extraction, which capture the mouth motions in an image sequence. The motivation for using motion features is, because the human perception of lip-reading is concerned with the temporal dynamics of mouth motion. The first approach is based on extraction of features from the optical flow vertical component. The optical flow vertical component is decomposed into multiple non-overlapping fixed scale blocks and statistical features of each block are computed for successive video frames of an utterance. To overcome the issue of large variation in speed of speech, each utterance is normalized using simple linear interpolation method. In the second approach, four directional motion templates based on optical flow are developed, each representing the consolidated motion information in an utterance in four directions (i.e.,up, down, left and right). This approach is an evolution of a view based approach known as motion history image (MHI). One of the main issues with the MHI method is its motion overwriting problem because of self-occlusion. DMHIs seem to solve this issue of overwriting. Two types of image descriptors, Zernike moments and Hu moments are used to represent each image of DMHIs. A support vector machine (SVM) classifier was used to classify the features obtained from the optical flow vertical component, Zernike and Hu moments separately. For identification of visemes, a multiclass SVM approach was employed. A video speech corpus of seven subjects was used for evaluating the efficiency of the proposed methods for lip-reading. The experimental results demonstrate the promising performance of the optical flow based mouth movement representations. Performance comparison between DMHI and MHI based on Zernike moments, shows that the DMHI technique outperforms the MHI technique. A video based adhoc temporal segmentation method is proposed in the thesis for isolated utterances. It has been used to detect the start and the end frame of an utterance from an image sequence. The technique is based on a pair-wise pixel comparison method. The efficiency of the proposed technique was tested on the available data set with short pauses between each utterance

Visual prosody in speech-driven facial animation: elicitation, prediction, and perceptual evaluation

Facial animations capable of articulating accurate movements in synchrony with a speech track have become a subject of much research during the past decade. Most of these efforts have focused on articulation of lip and tongue movements, since these are the primary sources of information in speech reading. However, a wealth of paralinguistic information is implicitly conveyed through visual prosody (e.g., head and eyebrow movements). In contrast with lip/tongue movements, however, for which the articulation rules are fairly well known (i.e., viseme-phoneme mappings, coarticulation), little is known about the generation of visual prosody. The objective of this thesis is to explore the perceptual contributions of visual prosody in speech-driven facial avatars. Our main hypothesis is that visual prosody driven by acoustics of the speech signal, as opposed to random or no visual prosody, results in more realistic, coherent and convincing facial animations. To test this hypothesis, we have developed an audio-visual system capable of capturing synchronized speech and facial motion from a speaker using infrared illumination and retro-reflective markers. In order to elicit natural visual prosody, a story-telling experiment was designed in which the actors were shown a short cartoon video, and subsequently asked to narrate the episode. From this audio-visual data, four different facial animations were generated, articulating no visual prosody, Perlin-noise, speech-driven movements, and ground truth movements. Speech-driven movements were driven by acoustic features of the speech signal (e.g., fundamental frequency and energy) using rule-based heuristics and autoregressive models. A pair-wise perceptual evaluation shows that subjects can clearly discriminate among the four visual prosody animations. It also shows that speech-driven movements and Perlin-noise, in that order, approach the performance of veridical motion. The results are quite promising and suggest that speech-driven motion could outperform Perlin-noise if more powerful motion prediction models are used. In addition, our results also show that exaggeration can bias the viewer to perceive a computer generated character to be more realistic motion-wise