About: Viseme is a(n) research topic. Over the lifetime, 865 publication(s) have been published within this topic receiving 17889 citation(s).
Papers published on a yearly basis
TL;DR: Whether or not, with similar acoustic differences, a listener can better discriminate betweenSounds that lie on opposite sides of a phoneme boundary than he can between sounds that fall within the same phoneme category is examined.
Abstract: In listening to speech, one typically reduces the number and variety of the many sounds with which he is bombarded by casting them into one or another of the phoneme categories that his language allows. Thus, a listener will identify as b, for example, quite a large number of acoustically different sounds. Although these differences are likely to be many and various, some of them will occur along an acoustic continuum that contains cues for a different phoneme, such as d. This is important for the present study because it provides a basis for the question to be examined here: whether or not, with similar acoustic differences, a listener can better discriminate between sounds that lie on opposite sides of a phoneme boundary than he can between sounds that fall within the same phoneme category. There are grounds for expecting an affirmative answer to this question. The most obvious, perhaps, are to be found in the common experience that in learning a new language one often
••01 Jul 2005
TL;DR: Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another, based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes.
Abstract: Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage The underlying face model automatically adjusts for how the target performs facial expressions and visemes The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable This supports a wide variety of video rewrite and puppetry applicationsFace Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (eg, identity, expression, and viseme) Separability means that each of these attributes can be independently varied A multilinear model can be estimated from a Cartesian product of examples (identities × expressions × visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters
•21 Jul 1994
Abstract: PART I: Speech and language 1. Communication 2. The production of speech 3. The sounds of speech 4. The description and classification of speech sounds 5. Sounds in language PART II: The sounds of English 6. The historical background 7. Standard and regional accents 8. The English vowels 9. The English consonants PART III: Words and connected speech 10. Words 11. Connected speech 12. Words in connected speech 13. Teaching the pronunciation of English
01 Jan 1989