| Patent application number | Description | Published |
|---|
| 20080201145 | Unsupervised labeling of sentence level accent - Methods are disclosed for automatic accent labeling without manually labeled data. The methods are designed to exploit accent distribution between function and content words. | 08-21-2008 |
| 20080208574 | Name synthesis - An automated method of providing a pronunciation of a word to a remote device is disclosed. The method includes receiving an input indicative of the word to be pronounced. The method further includes searching a database having a plurality of records. Each of the records has an indication of a textual representation and an associated indication of an audible representation. At least one output is provided to the remote device of an audible representation of the word to be pronounced. | 08-28-2008 |
| 20080240570 | SYMBOL GRAPH GENERATION IN HANDWRITTEN MATHEMATICAL EXPRESSION RECOGNITION - A forward pass through a sequence of strokes representing a handwritten equation is performed from the first stroke to the last stroke in the sequence. At each stroke, a path score is determined for a plurality of symbol-relation pairs that each represents a symbol and its spatial relation to a predecessor symbol. A symbol graph having nodes and links is constructed by backtracking through the strokes from the last stroke to the first stroke and assigning scores to the links based on the path scores for the symbol-relation pairs. The symbol graph is used to recognize a mathematical expression based in part on the scores for the links and the mathematical expression is stored. | 10-02-2008 |
| 20080243503 | MINIMUM DIVERGENCE BASED DISCRIMINATIVE TRAINING FOR PATTERN RECOGNITION - A method of providing discriminative training of a speech recognition unit is discussed. The method includes receiving an acoustic indication of an utterance having a hypothesis space and comparing the hypothesis space against a reference. The method measures the Kullback-Leibler Divergence (KLD) between the reference and the hypothesis space to adjust the reference and stores the adjusted reference on a tangible storage medium. | 10-02-2008 |
| 20090240501 | AUTOMATICALLY GENERATING NEW WORDS FOR LETTER-TO-SOUND CONVERSION - Described is a technology by which artificial words are generated based on seed words, and then used with a letter-to-sound conversion model. To generate an artificial word, a stressed syllable of a seed word is replaced with a different syllable, such as a candidate (artificial) syllable, when the phonemic structure and/or graphonemic structure of the stressed syllable and the candidate syllable match one another. In one aspect, the artificial words are provided for use with a letter-to-sound conversion model, which may be used to generate artificial phonemes from a source of words, such as in conjunction with other models. If the phonemes provided by the various models for a selected source word are in agreement relative to one another, the selected source word and an associated artificial phoneme may be added to a training set which may then be used to retrain the letter-to-sound conversion model. | 09-24-2009 |
| 20090245646 | Online Handwriting Expression Recognition - One way of recognizing online handwritten mathematical expressions is to use a one-pass dynamic programming based symbol decoding generation algorithm. This method embeds segmentation into symbol identification to form a unified framework for symbol recognition. Along with decoding, a symbol graph is produced. Besides accurately recognizing handwritten mathematical expressions, this method can produce high quality symbol graphs. This method uses six knowledge source models to help search for possible symbol hypotheses during the decoding process. Here, knowledge source exponential weights and a symbol insertion penalty are used to weigh the various knowledge source model probabilities to increase accuracy. | 10-01-2009 |
| 20090324082 | CHARACTER AUTO-COMPLETION FOR ONLINE EAST ASIAN HANDWRITING INPUT - An exemplary method includes receiving stroke information for a partially written East Asian character, the East Asian character representable by one or more radicals; based on the stroke information, selecting a radical on a prefix tree wherein the prefix tree branches to East Asian characters as end states; identifying one or more East Asian characters as end states that correspond to the selected radical for the partially written East Asian character; and receiving user input to verify that one of the identified one or more East Asian characters is the end state for the partially written East Asian character. In such a method, the selection of a radical can occur using radical-based hidden Markov models. Various other exemplary methods, devices, systems, etc., are also disclosed. | 12-31-2009 |
| 20100066742 | STYLIZED PROSODY FOR SPEECH SYNTHESIS-BASED APPLICATIONS - Described is a technology by which the prosody of synthesized speech may be changed by varying data associated with that speech. An interface displays a visual representation of synthesized speech as one or more waveforms, along with the corresponding text from which the speech was synthesized. The user may interact with the visual representation to change data corresponding to the prosody, e.g., to change duration, pitch and/or loudness data, with respect to a part (or all) of the speech. The part of the speech that may be varied may comprise a phoneme, a morpheme, a syllable, a word, a phrase, and/or a sentence. The changed speech can be played back to hear the change in prosody resulting from the interactive changes. The user can also change the text and hear/see newly synthesized speech, which may then be similarly edited to change data that corresponds to that speech's prosody. | 03-18-2010 |
| 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 |
| 20100166314 | Segment Sequence-Based Handwritten Expression Recognition - Methods and apparatuses for generating, by a computing device configured to interpret a handwritten expression, a symbol graph to represent strokes associated with the handwritten expression, are described herein. The symbol graph may include nodes, each node corresponding to a combination of a stroke and a candidate symbol for that stroke. The computing device may also generate a segment graph based on the symbol graph by combining nodes associated with a same stroke if strokes of their preceding nodes are the same. Also the computing device may perform a structure analysis on at least a subset of segment sequences represented by the segment graph to determine hypotheses for the handwritten expression. In other embodiments, rather than generate a segment graph, the computing device may determine segment sequences by selecting a number of symbol sequences from the symbol graph and combining symbol sequences having the same segmentation. | 07-01-2010 |
| 20100198577 | STATE MAPPING FOR CROSS-LANGUAGE SPEAKER ADAPTATION - Creation of sub-phonemic Hidden Markov Model (HMM) states and the mapping of those states results in improved cross-language speaker adaptation. The smaller sub-phonemic mapping provides improvements in usability and intelligibility particularly between languages with few common phonemes. HMM states of different languages may be mapped to one another using a distance between the HMM states in acoustic space. This distance may be calculated using Kullback-Leibler divergence and multi-space probability distribution. By combining distance mapping and context mapping for different speakers of the same language improved cross-language speaker adaptation is possible. | 08-05-2010 |
| 20110054903 | RICH CONTEXT MODELING FOR TEXT-TO-SPEECH ENGINES - Embodiments of rich text modeling for speech synthesis are disclosed. In operation, a text-to-speech engine refines a plurality of rich context models based on decision tree-tied Hidden Markov Models (HMMs) to produce a plurality of refined rich context models. The text-to-speech engine then generates synthesized speech for an input text based at least on some of the plurality of refined rich context models. | 03-03-2011 |
| 20110071835 | SMALL FOOTPRINT TEXT-TO-SPEECH ENGINE - Embodiments of small footprint text-to-speech engine are disclosed. In operation, the small footprint text-to-speech engine generates a set of feature parameters for an input text. The set of feature parameters includes static feature parameters and delta feature parameters. The small footprint text-to-speech engine then derives a saw-tooth stochastic trajectory that represents the speech characteristics of the input text based on the static feature parameters and the delta parameters. Finally, the small footprint text-to-speech engine produces a smoothed trajectory from the saw-tooth stochastic trajectory, and generates synthesized speech based on the smoothed trajectory. | 03-24-2011 |
