Patent application number | Description | Published |
20090083036 | Unnatural prosody detection in speech synthesis - Described is a technology by which synthesized speech generated from text is evaluated against a prosody model (trained offline) to determine whether the speech will sound unnatural. If so, the speech is regenerated with modified data. The evaluation and regeneration may be iterative until deemed natural sounding. For example, text is built into a lattice that is then (e.g., Viterbi) searched to find a best path. The sections (e.g., units) of data on the path are evaluated via a prosody model. If the evaluation deems a section to correspond to unnatural prosody, that section is replaced, e.g., by modifying/pruning the lattice and re-performing the search. Replacement may be iterative until all sections pass the evaluation. Unnatural prosody detection may be biased such that during evaluation, unnatural prosody is falsely detected at a higher rate relative to a rate at which unnatural prosody is missed. | 03-26-2009 |
20090099847 | Template constrained posterior probability - Detailed herein is a technology which, among other things, reduces errors introduced in recording and transcription data. In one approach to this technology, a method of detecting audio transcription errors is utilized. This method includes selected a focus unit, and selecting a context template corresponding to the focus unit. A hypothesis set is then determined, with reference to the context template and the focus unit. A probability is calculated corresponding to the focus unit, across the hypothesis set. | 04-16-2009 |
20100082345 | SPEECH AND TEXT DRIVEN HMM-BASED BODY ANIMATION SYNTHESIS - An “Animation Synthesizer” uses trainable probabilistic models, such as Hidden Markov Models (HMM), Artificial Neural Networks (ANN), etc., to provide speech and text driven body animation synthesis. Probabilistic models are trained using synchronized motion and speech inputs (e.g., live or recorded audio/video feeds) at various speech levels, such as sentences, phrases, words, phonemes, sub-phonemes, etc., depending upon the available data, and the motion type or body part being modeled. The Animation Synthesizer then uses the trainable probabilistic model for selecting animation trajectories for one or more different body parts (e.g., face, head, hands, arms, etc.) based on an arbitrary text and/or speech input. These animation trajectories are then used to synthesize a sequence of animations for digital avatars, cartoon characters, computer generated anthropomorphic persons or creatures, actual motions for physical robots, etc., that are synchronized with a speech output corresponding to the text and/or speech input. | 04-01-2010 |
20120116761 | Minimum Converted Trajectory Error (MCTE) Audio-to-Video Engine - Embodiments of an audio-to-video engine are disclosed. In operation, the audio-to-video engine generates facial movement (e.g., a virtual talking head) based on an input speech. The audio-to-video engine receives the input speech and recognizes the input speech as a source feature vector. The audio-to-video engine then determines a Maximum A Posterior (MAP) mixture sequence based on the source feature vector. The MAP mixture sequence may be a function of a refined Gaussian Mixture Model (GMM). The audio-to-video engine may then use the MAP to estimate video feature parameters. The video feature parameters are then interpreted as facial movement. The facial movement may be stored as data to a storage module and/or it may be displayed as video to a display device. | 05-10-2012 |
20120130717 | Real-time Animation for an Expressive Avatar - Techniques for providing real-time animation for a personalized cartoon avatar are described. In one example, a process trains one or more animated models to provide a set of probabilistic motions of one or more upper body parts based on speech and motion data. The process links one or more predetermined phrases that represent emotional states to the one or more animated models. After creation of the models, the process receives real-time speech input. Next, the process identifies an emotional state to be expressed based on the one or more predetermined phrases matching in context to the real-time speech input. The process then generates an animated sequence of motions of the one or more upper body parts by applying the one or more animated models in response to the real-time speech input. | 05-24-2012 |
20120276504 | Talking Teacher Visualization for Language Learning - A representation of a virtual language teacher assists in language learning. The virtual language teacher may appear as a “talking head” in a video that a student views to practice pronunciation of a foreign language. A system for generating a virtual language teacher receives input text. The system may generate a video showing the virtual language teacher as a talking head having a mouth that moves in synchronization with speech generated from the input text. The video of the virtual language teacher may then be presented to the student. | 11-01-2012 |
20120280974 | PHOTO-REALISTIC SYNTHESIS OF THREE DIMENSIONAL ANIMATION WITH FACIAL FEATURES SYNCHRONIZED WITH SPEECH - Dynamic texture mapping is used to create a photorealistic three dimensional animation of an individual with facial features synchronized with desired speech. Audiovisual data of an individual reading a known script is obtained and stored in an audio library and an image library. The audiovisual data is processed to extract feature vectors used to train a statistical model. An input audio feature vector corresponding to desired speech with which the animation will be synchronized is provided. The statistical model is used to generate a trajectory of visual feature vectors that corresponds to the input audio feature vector. These visual feature vectors are used to identify a matching image sequence from the image library. The resulting sequence of images, concatenated from the image library, provides a photorealistic image sequence with facial features, such as lip movements, synchronized with the desired speech. This image sequence is applied to the three-dimensional model. | 11-08-2012 |
20120284029 | PHOTO-REALISTIC SYNTHESIS OF IMAGE SEQUENCES WITH LIP MOVEMENTS SYNCHRONIZED WITH SPEECH - Audiovisual data of an individual reading a known script is obtained and stored in an audio library and an image library. The audiovisual data is processed to extract feature vectors used to train a statistical model. An input audio feature vector corresponding to desired speech with which a synthesized image sequence will be synchronized is provided. The statistical model is used to generate a trajectory of visual feature vectors that corresponds to the input audio feature vector. These visual feature vectors are used to identify a matching image sequence from the image library. The resulting sequence of images, concatenated from the image library, provides a photorealistic image sequence with lip movements synchronized with the desired speech. | 11-08-2012 |
20140025381 | EVALUATING TEXT-TO-SPEECH INTELLIGIBILITY USING TEMPLATE CONSTRAINED GENERALIZED POSTERIOR PROBABILITY - Instead of relying on humans to subjectively evaluate speech intelligibility of a subject, a system objectively evaluates the speech intelligibility. The system receives speech input and calculates confidence scores at multiple different levels using a Template Constrained Generalized Posterior Probability algorithm. One or multiple intelligibility classifiers are utilized to classify the desired entities on an intelligibility scale. A specific intelligibility classifier utilizes features such as the various confidence scores. The scale of the intelligibility classification can be adjusted to suit the application scenario. Based on the confidence score distributions and the intelligibility classification results at multiple levels an overall objective intelligibility score is calculated. The objective intelligibility scores can be used to rank different subjects or systems being assessed according to their intelligibility levels. The speech that is below a predetermined intelligibility (e.g. utterances with low confidence scores and most severe intelligibility issues) can be automatically selected for further analysis. | 01-23-2014 |