An interactive speech training system with virtual reality articulation for Mandarin-speaking hearing impaired children

The present project involved the development of a novel interactive speech training system based on virtual reality articulation and examination of the efficacy of the system for hearing impaired (HI) children. Twenty meaningful Mandarin words were presented to the HI children via a 3-D talking head during articulation training. Electromagnetic Articulography (EMA) and graphic transform technology were used to depict movements of various articulators. In addition, speech corpuses were organized in listening and speaking training modules of the system to help improve language skills of the HI children. Accuracy of virtual reality articulatory movement was evaluated through a series of experiments. Finally, a pilot test was performed to train two HI children using the system. Preliminary results showed improvement in speech production by the HI children, and the system was recognized as acceptable and interesting for children. It can be concluded that the training system is effective and valid in articulation training for HI children. © 2013 IEEE.published_or_final_versio

Example Based Caricature Synthesis

The likeness of a caricature to the original face image is an essential and often overlooked part of caricature production. In this paper we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial features. The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-Shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so called proportions, which characterizes the facial features in a proportion form. Finally we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results

Speaker Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the determination of articulatory parameters from acoustic signals, is a difficult but important problem for many speech processing applications, such as automatic speech recognition (ASR) and computer aided pronunciation training (CAPT). In recent years, several approaches have been successfully implemented for speaker dependent models with parallel acoustic and kinematic training data. However, in many practical applications inversion is needed for new speakers for whom no articulatory data is available. In order to address this problem, this dissertation introduces a novel speaker adaptation approach called Parallel Reference Speaker Weighting (PRSW), based on parallel acoustic and articulatory Hidden Markov Models (HMM). This approach uses a robust normalized articulatory space and palate referenced articulatory features combined with speaker-weighted adaptation to form an inversion mapping for new speakers that can accurately estimate articulatory trajectories. The proposed PRSW method is evaluated on the newly collected Marquette electromagnetic articulography - Mandarin Accented English (EMA-MAE) corpus using 20 native English speakers. Cross-speaker inversion results show that given a good selection of reference speakers with consistent acoustic and articulatory patterns, the PRSW approach gives good speaker independent inversion performance even without kinematic training data

Tongue control and its implication in pronunciation training

International audiencePronunciation training based on speech production techniques illustrating tongue movements is gaining popularity. However, there is not sufficient evidence that learners can imitate some tongue animation. In this paper, we argue that although controlling tongue movement related to speech is not such an easy task, training with visual feedback improves its control. We investigated human awareness of controlling their tongue body gestures. In a first experiment, participants were asked to perform some tongue movements composed of two sets of gestures. This task was evaluated by observing ultrasound imaging of the tongue recorded during the experiment. No feedback was provided. In a second experiment, a short session of training was added where participants can observe ultrasound imaging in real time of their own tongue movements. The goal was to increase their awareness of their tongue gestures. A pretest and posttest were carried out without any feedback. The results suggest that without a priori knowledge, it is not easy to finely control tongue body gestures. The second experiment showed that we gained in performance after a short training session and this suggests that providing visual feedback, even a short one, improves tongue gesture awareness

Lip syncing method for realistic expressive 3D face model

Lip synchronization of 3D face model is now being used in a multitude of important fields. It brings a more human, social and dramatic reality to computer games, films and interactive multimedia, and is growing in use and importance. High level of realism can be used in demanding applications such as computer games and cinema. Authoring lip syncing with complex and subtle expressions is still difficult and fraught with problems in terms of realism. This research proposed a lip syncing method of realistic expressive 3D face model. Animated lips requires a 3D face model capable of representing the myriad shapes the human face experiences during speech and a method to produce the correct lip shape at the correct time. The paper presented a 3D face model designed to support lip syncing that align with input audio file. It deforms using Raised Cosine Deformation (RCD) function that is grafted onto the input facial geometry. The face model was based on MPEG-4 Facial Animation (FA) Standard. This paper proposed a method to animate the 3D face model over time to create animated lip syncing using a canonical set of visemes for all pairwise combinations of a reduced phoneme set called ProPhone. The proposed research integrated emotions by the consideration of Ekman model and Plutchik’s wheel with emotive eye movements by implementing Emotional Eye Movements Markup Language (EEMML) to produce realistic 3D face model. © 2017 Springer Science+Business Media New Yor

Investigating 3D Visual Speech Animation Using 2D Videos

Lip motion accuracy is of paramount importance for speech intelligibility, especially for users who are hard of hearing or foreign language learners. Furthermore, generating a high level of realism in lip movements is required for the game and film production industries. This thesis focuses on the mapping of tracked lip motions of front-view 2D videos of a real speaker to a synthetic 3D head. A data-driven approach is used based on a 3D morphable model (3DMM) built using 3D synthetic head poses. The 3DMMs have been widely used for different tasks such as face recognition, detect facial expressions and lip motions in 2D videos. However, investigating factors such as the required facial landmarks for the mapping process, the amount of data for constructing the 3DMM, and differences in facial features between real faces and 3D faces that may influence the resulting animation have not been considered yet. Therefore, this research centers around investigating the impact of these factors on the final 3D lip motions. The thesis explores how different sets of facial features used in the mapping process influence the resulting 3D motions. Five sets of the facial features are used for mapping the real faces to the corresponding 3D faces. The results show that the inclusion of eyebrows, eyes, nose, and lips improves the 3D lip motions, while face contour features (i.e. the outside boundary of the front view of the face) restrict the face’s mesh, distorting the resulting animation. This thesis investigates how using different amounts of data when constructing the 3DMM affects the 3D lip motions. The results 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-view photographs of real speakers to produce initial neutral 3D synthetic head poses, provides better animation results compared to ground truth data consisting of front- and side-view 2D videos of real speakers. The thesis also investigates the impact of differences and similarities in facial features between real speakers and the 3DMMs on the resulting 3D lip motions by mapping between non-similar faces based on differences and similarities in vertical mouth height and mouth width. The objective and user test results show that mapping 2D videos of real speakers with low vertical mouth heights to 3D heads that correspond to real speakers with high vertical mouth heights, or vice versa, generates less good 3D lip motions. It is thus important that this is considered when using a 2D recording of a real actor’s lip movements to control a 3D synthetic character

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