Acoustic measurements believed to reflect glottal characteristics were made on recordings collected from 21 male speakers. The waveforms and spectra of three nonhigh vowels (/ae, lambda, epsilon/) were analyzed to obtain acoustic parameters related to first-formant bandwidth, open quotient, spectral tilt, and aspiration noise. Comparisons were made with previous results obtained for 22 female speakers [H. M. Hanson, J. Acoust. Soc. Am. 101, 466-481 (1997)]. While there is considerable overlap across gender, the male data show lower average values and less interspeaker variation for all measures. In particular, the amplitude of the first harmonic relative to that of the third formant is 9.6 dB lower for the male speakers than for the female speakers, suggesting that spectral tilt is an especially significant parameter for differentiating male and female speech. These findings are consistent with fiberscopic studies which have shown that males tend to have a more complete glottal closure, leading to less energy loss at the glottis and less spectral tilt. Observations of the speech waveforms and spectra suggest the presence of a second glottal excitation within a glottal period for some of the male speakers. Possible causes and acoustic consequences of these second excitations are discussed.
We introduce a new interface for rapidly creating 3D articulated figure animation, from 2D sketches of the character in the desired key frame poses. Since the exact 3D animation corresponding to a set of 2D drawings is ambiguous we first reconstruct the possible 3D configurations and then apply a set of constraints and assumptions to present the user with the most likely 3D pose. The user can refine this candidate pose by choosing among alternate poses proposed by the system. This interface is supported by pose reconstruction and optimization methods specifically designed to work with imprecise hand drawn figures. Our system provides a simple, intuitive and fast interface for creating rough animations that leverages our users' existing ability to draw. The resulting key framed sequence can be exported to commercial animation packages for interpolation and additional refinement.
Motion capture-based facial animation has recently gained popularity in many applications, such as movies, video games, and human-computer interface designs. With the use of sophisticated facial motions from a human performer, animated characters are far more lively and convincing. However, editing motion data is difficult, limiting the potential of reusing the motion data for different tasks. To address this problem, statistical techniques have been applied to learn models of the facial motion in order to derive new motions based on the existing data. Most existing research focuses on audio-to-visual mapping and reordering of words, or on photo-realistically matching the synthesized face to the original performer. Little attention has been paid to modifying and controlling facial expression, or to mapping expressive motion onto other 3D characters.This article describes a method for creating expressive facial animation by extracting information from the expression axis of a speech performance. First, a statistical model for factoring the expression and visual speech is learned from video. This model can be used to analyze the facial expression of a new performance or modify the facial expressions of an existing performance. With the addition of this analysis of the facial expression, the facial motion can be more effectively retargeted to another 3D face model. The blendshape retargeting technique is extended to include subsets of morph targets that belong to different facial expression groups. The proportion of each subset included in a final animation is weighted according to the expression information. The resulting animation conveys much more emotion than if only the motion vectors were used for retargeting. Finally, since head motion is very important in adding liveness to facial animation, we introduces an audio-driven synthesis technique for generating new head motion.
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