Patents by Inventor Guillermo Aradilla
Guillermo Aradilla has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20230230404Abstract: A method for recognizing handwritten text is disclosed. The method comprises receiving data comprising a sequence of ink points; applying the received data to a neural network-based sequence classifier trained with a Connectionist Temporal Classification (CTC) output layer using forced alignment to generate an output; generating a character hypothesis as a portion of the sequence of ink points; applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes the given character; processing the output of the CTC output layer to determine a second probability corresponding to the probability that the given character is observed within the character hypothesis; and combining the first probability and the second probability to obtain a combined probability corresponding to the probability that the character hypothesis includes the given character.Type: ApplicationFiled: May 11, 2021Publication date: July 20, 2023Inventors: Gaël MANSON, Guillermo ARADILLA, Cyril CEROVIC, Pierre-Michel LALLICAN
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Patent number: 10884610Abstract: A system, method and computer program product for use in recognizing content associated with handwritten stroke input to a computing device is provided. The computing device is connected to an input interface. A user is able to provide input by applying pressure to or gesturing above the input interface using a finger or an instrument such as a stylus or pen. The computing device has an input management system for recognizing content defined by the input. The input management system is configured to detect input of a handwritten stroke with respect to the interactive key layout, characterize the detected handwritten stroke by a reference stroke by determining a sequence of reference points associated with a sequence of interactive keys of the interactive key layout, assign probability scores to candidate characters, and cause recognition of sequences of characters by applying a language model in accordance with the assigned probability scores.Type: GrantFiled: March 22, 2017Date of Patent: January 5, 2021Assignee: MYSCRIPTInventors: Guillermo Aradilla, Gael Manson
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Patent number: 10007859Abstract: A system and method that is able to recognize a user's natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. It can also process cursive handwriting. Further, the method and system can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of fragmentation, segmentation, character recognition, and language modeling. At least some of these processes occur concurrently through the use of dynamic programming.Type: GrantFiled: March 28, 2016Date of Patent: June 26, 2018Assignee: MyScriptInventors: Zsolt Wimmer, Freddy Perraud, Pierre-Michel Lallican, Guillermo Aradilla
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Publication number: 20180129408Abstract: A system, method and computer program product for use in recognizing content associated with handwritten stroke input to a computing device is provided. The computing device is connected to an input interface. A user is able to provide input by applying pressure to or gesturing above the input interface using a finger or an instrument such as a stylus or pen. The computing device has an input management system for recognizing content defined by the input. The input management system is configured to detect input of a handwritten stroke with respect to the interactive key layout, characterize the detected handwritten stroke by a reference stroke by determining a sequence of reference points associated with a sequence of interactive keys of the interactive key layout, assign probability scores to candidate characters, and cause recognition of sequences of characters by applying a language model in accordance with the assigned probability scores.Type: ApplicationFiled: March 22, 2017Publication date: May 10, 2018Inventors: Guillermo Aradilla, Gael Manson
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Patent number: 9595257Abstract: An approach for phoneme recognition is described. A sequence of intermediate output posterior vectors is generated from an input sequence of cepstral features using a first layer perceptron. The intermediate output posterior vectors are then downsampled to form a reduced input set of intermediate posterior vectors for a second layer perceptron. A sequence of final posterior vectors is generated from the reduced input set of intermediate posterior vectors using the second layer perceptron. Then the final posterior vectors are decoded to determine an output recognized phoneme sequence representative of the input sequence of cepstral features.Type: GrantFiled: September 28, 2009Date of Patent: March 14, 2017Assignee: Nuance Communications, Inc.Inventors: Daniel Andrés Vásquez Cano, Guillermo Aradilla, Rainer Gruhn
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Publication number: 20160275364Abstract: A system and method that is able to recognize a user's natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. It can also process cursive handwriting. Further, the method and system can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of fragmentation, segmentation, character recognition, and language modeling. At least some of these processes occur concurrently through the use of dynamic programming.Type: ApplicationFiled: March 28, 2016Publication date: September 22, 2016Inventors: Zsolt WIMMER, Freddy Perraud, Pierre-Michel Lallican, Guillermo Aradilla
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Patent number: 9384403Abstract: A system and method that is able to recognize a user's natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. It can also process cursive handwriting. Further, the method and system can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of fragmentation, segmentation, character recognition, and language modeling. At least some of these processes occur concurrently through the use of dynamic programming.Type: GrantFiled: July 10, 2015Date of Patent: July 5, 2016Assignee: MYSCRIPTInventors: Zsolt Wimmer, Freddy Perraud, Pierre-Michel Lallican, Guillermo Aradilla
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Patent number: 9318103Abstract: An automatic speech recognition system for recognizing a user voice command in noisy environment, including: matching means for matching elements retrieved from speech units forming said command with templates in a template library; characterized by processing means including a MultiLayer Perceptron for computing posterior templates (P(Otemplate(q))) stored as said templates in said template library; means for retrieving posterior vectors (P(Otest(q))) from said speech units, said posterior vectors being used as said elements. The present invention relates also to a method for recognizing a user voice command in noisy environments.Type: GrantFiled: February 21, 2013Date of Patent: April 19, 2016Assignee: VEOVOX SAInventors: John Dines, Jorge Carmona, Olivier Masson, Guillermo Aradilla
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Publication number: 20150356360Abstract: A system and method that is able to recognize a user's natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. It can also process cursive handwriting. Further, the method and system can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of fragmentation, segmentation, character recognition, and language modeling. At least some of these processes occur concurrently through the use of dynamic programming.Type: ApplicationFiled: July 10, 2015Publication date: December 10, 2015Inventors: Zsolt WIMMER, Freddy PERRAUD, Pierre-Michel LALLICAN, Guillermo ARADILLA
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Patent number: 8554555Abstract: The invention provides a method for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame. A sequence of frames from the training data are provided, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks. Each of the artificial neural networks is assigned a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames. A common phoneme label for the sequence of frames is determined based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence. Each artificial neural network using the common phoneme label.Type: GrantFiled: February 17, 2010Date of Patent: October 8, 2013Assignee: Nuance Communications, Inc.Inventors: Rainer Gruhn, Daniel Vasquez, Guillermo Aradilla
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Publication number: 20120245919Abstract: An automatic speech recognition (ASR) apparatus for an embedded device application is described. A speech decoder receives an input sequence of speech feature vectors in a first language and outputs an acoustic segment lattice representing a probabilistic combination of basic linguistic units in a second language. A vocabulary matching module compares the acoustic segment lattice to vocabulary models in the first language to determine an output set of probability-ranked recognition hypotheses. A detailed matching module compares the set of probability-ranked recognition hypotheses to detailed match models in the first language to determine a recognition output representing a vocabulary word most likely to correspond to the input sequence of speech feature vectors.Type: ApplicationFiled: September 23, 2009Publication date: September 27, 2012Applicant: NUANCE COMMUNICATIONS, INC.Inventors: Guillermo Aradilla, Rainer Gruhn
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Publication number: 20120239403Abstract: An approach for phoneme recognition is described. A sequence of intermediate output posterior vectors is generated from an input sequence of cepstral features using a first layer perceptron. The intermediate output posterior vectors are then downsampled to form a reduced input set of intermediate posterior vectors for a second layer perceptron. A sequence of final posterior vectors is generated from the reduced input set of intermediate posterior vectors using the second layer perceptron. Then the final posterior vectors are decoded to determine an output recognized phoneme sequence representative of the input sequence of cepstral features.Type: ApplicationFiled: September 28, 2009Publication date: September 20, 2012Applicant: NUANCE COMMUNICATIONS, INC.Inventors: Daniel Andrés Vásquez Cano, Guillermo Aradilla, Rainer Gruhn
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Publication number: 20100217589Abstract: The invention provides a method for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame. A sequence of frames from the training data are provided, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks. Each of the artificial neural networks is assigned a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames. A common phoneme label for the sequence of frames is determined based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence. Each artificial neural network using the common phoneme label.Type: ApplicationFiled: February 17, 2010Publication date: August 26, 2010Applicant: NUANCE COMMUNICATIONS, INC.Inventors: Rainer Gruhn, Daniel Vasquez, Guillermo Aradilla