Enhancing Expressiveness of Speech through Animated Avatars for Instant Messaging and Mobile Phones

This thesis aims to create a chat program that allows users to communicate via an animated avatar that provides believable lip-synchronization and expressive emotion. Currently many avatars do not attempt to do lip-synchronization. Those that do are not well synchronized and have little or no emotional expression. Most avatars with lip synch use realistic looking 3D models or stylized rendering of complex models. This work utilizes images rendered in a cartoon style and lip-synchronization rules based on traditional animation. The cartoon style, as opposed to a more realistic look, makes the mouth motion more believable and the characters more appealing. The cartoon look and image-based animation (as opposed to a graphic model animated through manipulation of a skeleton or wireframe) also allows for fewer key frames resulting in faster speed with more room for expressiveness. When text is entered into the program, the Festival Text-to-Speech engine creates a speech file and extracts phoneme and phoneme duration data. Believable and fluid lip-synchronization is then achieved by means of a number of phoneme-to-image rules. Alternatively, phoneme and phoneme duration data can be obtained for speech dictated into a microphone using Microsoft SAPI and the CSLU Toolkit. Once lip synchronization has been completed, rules for non-verbal animation are added. Emotions are appended to the animation of speech in two ways: automatically, by recognition of key words and punctuation, or deliberately, by user-defined tags. Additionally, rules are defined for idle-time animation. Preliminary results indicate that the animated avatar program offers an improvement over currently available software. It aids in the understandability of speech, combines easily recognizable and expressive emotions with speech, and successfully enhances overall enjoyment of the chat experience. Applications for the program include use in cell phones for the deaf or hearing impaired, instant messaging, video conferencing, instructional software, and speech and animation synthesis

Expressive Modulation of Neutral Visual Speech

The need for animated graphical models of the human face is commonplace in the movies, video games and television industries, appearing in everything from low budget advertisements and free mobile apps, to Hollywood blockbusters costing hundreds of millions of dollars. Generative statistical models of animation attempt to address some of the drawbacks of industry standard practices such as labour intensity and creative inflexibility. This work describes one such method for transforming speech animation curves between different expressive styles. Beginning with the assumption that expressive speech animation is a mix of two components, a high-frequency speech component (the content) and a much lower-frequency expressive component (the style), we use Independent Component Analysis (ICA) to identify and manipulate these components independently of one another. Next we learn how the energy for different speaking styles is distributed in terms of the low-dimensional independent components model. Transforming the speaking style involves projecting new animation curves into the lowdimensional ICA space, redistributing the energy in the independent components, and finally reconstructing the animation curves by inverting the projection. We show that a single ICA model can be used for separating multiple expressive styles into their component parts. Subjective evaluations show that viewers can reliably identify the expressive style generated using our approach, and that they have difficulty in identifying transformed animated expressive speech from the equivalent ground-truth

Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

Text-based Editing of Talking-head Video

Editing talking-head video to change the speech content or to remove filler words is challenging. We propose a novel method to edit talking-head video based on its transcript to produce a realistic output video in which the dialogue of the speaker has been modified, while maintaining a seamless audio-visual flow (i.e. no jump cuts). Our method automatically annotates an input talking-head video with phonemes, visemes, 3D face pose and geometry, reflectance, expression and scene illumination per frame. To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material. The annotated parameters corresponding to the selected segments are seamlessly stitched together and used to produce an intermediate video representation in which the lower half of the face is rendered with a parametric face model. Finally, a recurrent video generation network transforms this representation to a photorealistic video that matches the edited transcript. We demonstrate a large variety of edits, such as the addition, removal, and alteration of words, as well as convincing language translation and full sentence synthesis

Reactive Statistical Mapping: Towards the Sketching of Performative Control with Data

Part 1: Fundamental IssuesInternational audienceThis paper presents the results of our participation to the ninth eNTERFACE workshop on multimodal user interfaces. Our target for this workshop was to bring some technologies currently used in speech recognition and synthesis to a new level, i.e. being the core of a new HMM-based mapping system. The idea of statistical mapping has been investigated, more precisely how to use Gaussian Mixture Models and Hidden Markov Models for realtime and reactive generation of new trajectories from inputted labels and for realtime regression in a continuous-to-continuous use case. As a result, we have developed several proofs of concept, including an incremental speech synthesiser, a software for exploring stylistic spaces for gait and facial motion in realtime, a reactive audiovisual laughter and a prototype demonstrating the realtime reconstruction of lower body gait motion strictly from upper body motion, with conservation of the stylistic properties. This project has been the opportunity to formalise HMM-based mapping, integrate various of these innovations into the Mage library and explore the development of a realtime gesture recognition tool