Videorealistic facial animation for speech-based interfaces

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 79-81).This thesis explores the use of computer-generated, videorealistic facial animation (avatars) in speech-based interfaces to understand whether the use of such animations enhances the end user's experience. Research in spoken dialog systems is a robust area that has now permeated everyday life; most notably with spoken telephone dialog systems. Over the past decade, research with videorealistic animations, both photorealistic and non-photorealistic, has reached the point where there is little discernible difference between the mouth movements of videorealistic animations and the mouth movements of actual humans. Because of the minute differences between the two, videorealistic speech animations are an ideal candidate to use in dialog systems. This thesis presents two videorealistic facial animation systems: a web-based system and a real-time system.by Stephen J. Pueblo.M.Eng

Speech-driven Lip Motion Generation with a Trajectory HMM

Automatic speech animation remains a challenging problem that can be described as finding the optimal sequence of animation parameter configurations given some speech. In this paper we present a novel technique to automatically synthesise lip motion trajectories from a speech signal. The developed system predicts lip motion units from the speech signal and generates animation trajectories automatically employing a Trajectory Hidden Markov Model. Using the MLE criterion, its parameter generation algorithm produces the optimal smooth motion trajectories that are used to drive control points on the lips directly. Additionally, experiments were carried out to find a suitable model unit that produces the most accurate results. Finally a perceptual evaluation was conducted, that showed that the developed motion units perform better than phonemes

New method for mathematical modelling of human visual speech

Audio-visual speech recognition and visual speech synthesisers are used as interfaces between humans and machines. Such interactions specifically rely on the analysis and synthesis of both audio and visual information, which humans use for face-to-face communication. Currently, there is no global standard to describe these interactions nor is there a standard mathematical tool to describe lip movements. Furthermore, the visual lip movement for each phoneme is considered in isolation rather than a continuation from one to another. Consequently, there is no globally accepted standard method for representing lip movement during articulation. This thesis addresses these issues by designing a transcribed group of words, by mathematical formulas, and so introducing the concept of a visual word, allocating signatures to visual words and finally building a visual speech vocabulary database. In addition, visual speech information has been analysed in a novel way by considering both lip movements and phonemic structure of the English language. In order to extract the visual data, three visual features on the lip have been chosen; these are on the outer upper, lower and corner of the lip. The extracted visual data during articulation is called the visual speech sample set. The final visual data is obtained after processing the visual speech sample sets to correct experimented artefacts such as head tilting, which happened during articulation and visual data extraction. The ‘Barycentric Lagrange Interpolation’ (BLI) formulates the visual speech sample sets into visual speech signals. The visual word is defined in this work and consists of the variation of three visual features. Further processing on relating the visual speech signals to the uttered word leads to the allocation of signatures that represent the visual word. This work suggests the visual word signature can be used either as a ‘visual word barcode’, a ‘digital visual word’ or a ‘2D/3D representations’. The 2D version of the visual word provides a unique signature that allows the identification of the words being uttered. In addition, identification of visual words has also been performed using a technique called ‘volumetric representations of the visual words’. Furthermore, the effect of altering the amplitudes and sampling rate for BLI has been evaluated. In addition, the performance of BLI in reconstructing the visual speech sample sets has been considered. Finally, BLI has been compared to signal reconstruction approach by RMSE and correlation coefficients. The results show that the BLI is the more reliable method for the purpose of this work according to Section 7.7

New method for mathematical modelling of human visual speech

Audio-visual speech recognition and visual speech synthesisers are used as interfaces between humans and machines. Such interactions specifically rely on the analysis and synthesis of both audio and visual information, which humans use for face-to-face communication. Currently, there is no global standard to describe these interactions nor is there a standard mathematical tool to describe lip movements. Furthermore, the visual lip movement for each phoneme is considered in isolation rather than a continuation from one to another. Consequently, there is no globally accepted standard method for representing lip movement during articulation. This thesis addresses these issues by designing a transcribed group of words, by mathematical formulas, and so introducing the concept of a visual word, allocating signatures to visual words and finally building a visual speech vocabulary database. In addition, visual speech information has been analysed in a novel way by considering both lip movements and phonemic structure of the English language. In order to extract the visual data, three visual features on the lip have been chosen; these are on the outer upper, lower and corner of the lip. The extracted visual data during articulation is called the visual speech sample set. The final visual data is obtained after processing the visual speech sample sets to correct experimented artefacts such as head tilting, which happened during articulation and visual data extraction. The ‘Barycentric Lagrange Interpolation’ (BLI) formulates the visual speech sample sets into visual speech signals. The visual word is defined in this work and consists of the variation of three visual features. Further processing on relating the visual speech signals to the uttered word leads to the allocation of signatures that represent the visual word. This work suggests the visual word signature can be used either as a ‘visual word barcode’, a ‘digital visual word’ or a ‘2D/3D representations’. The 2D version of the visual word provides a unique signature that allows the identification of the words being uttered. In addition, identification of visual words has also been performed using a technique called ‘volumetric representations of the visual words’. Furthermore, the effect of altering the amplitudes and sampling rate for BLI has been evaluated. In addition, the performance of BLI in reconstructing the visual speech sample sets has been considered. Finally, BLI has been compared to signal reconstruction approach by RMSE and correlation coefficients. The results show that the BLI is the more reliable method for the purpose of this work according to Section 7.7