Patents by Inventor James G. Droppo
James G. Droppo 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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Patent number: 10885438Abstract: A neural network is structured with a plurality of levels of nodes. Each level has a level-specific stabilization parameter that adjusts a learning rate, at a corresponding level, during training. The stabilization parameter has a value that varies inversely relative to a change in an objective training function during back-propagation of the error through the level.Type: GrantFiled: December 28, 2015Date of Patent: January 5, 2021Assignee: Microsoft Technology Licensing, LLCInventors: James G. Droppo, Pegah Ghahremani, Avner May
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Patent number: 10558909Abstract: A neural network is structured to connect the input values of an input set, at each level, to that level's output using a linear bypass connection. The linear bypass connection passes the input values, to the output, without applying a non-linear function to them.Type: GrantFiled: December 28, 2015Date of Patent: February 11, 2020Assignee: Microsoft Technology Licensing, LLCInventors: James G. Droppo, Pegah Ghahremani, Michael L. Seltzer
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Patent number: 9824684Abstract: A sequence recognition system comprises a prediction component configured to receive a set of observed features from a signal to be recognized and to output a prediction output indicative of a predicted recognition based on the set of observed features. The sequence recognition system also comprises a classification component configured to receive the prediction output and to output a label indicative of recognition of the signal based on the prediction output.Type: GrantFiled: December 22, 2014Date of Patent: November 21, 2017Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Dong Yu, Yu Zhang, Michael L. Seltzer, James G. Droppo
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Patent number: 9786284Abstract: This document describes various techniques for dual-band speech encoding. In some embodiments, a first type of speech feature is received from a remote entity, an estimate of a second type of speech feature is determined based on the first type of speech feature, the estimate of the second type of speech feature is provided to a speech recognizer, speech-recognition results based on the estimate of the second type of speech feature are received from the speech recognizer, and the speech-recognition results are transmitted to the remote entity.Type: GrantFiled: August 14, 2014Date of Patent: October 10, 2017Assignee: Microsoft Technology Licensing, LLCInventors: Alejandro Acero, James G. Droppo, III, Michael L. Seltzer
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Publication number: 20170185897Abstract: A neural network is structured with a plurality of levels of nodes. Each level has a level-specific stabilization parameter that adjusts a learning rate, at a corresponding level, during training.Type: ApplicationFiled: December 28, 2015Publication date: June 29, 2017Inventors: James G. Droppo, Pegah Ghahremani, Avner May
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Publication number: 20170185887Abstract: A neural network is structured to connect the input values of an input set, at each level, to that level's output using a linear bypass connection. The linear bypass connection passes the input values, to the output, without applying a non-linear function to them.Type: ApplicationFiled: December 28, 2015Publication date: June 29, 2017Inventors: James G. Droppo, Pegah Ghahremani, Michael L. Seltzer
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Publication number: 20160140956Abstract: A sequence recognition system comprises a prediction component configured to receive a set of observed features from a signal to be recognized and to output a prediction output indicative of a predicted recognition based on the set of observed features. The sequence recognition system also comprises a classification component configured to receive the prediction output and to output a label indicative of recognition of the signal based on the prediction output.Type: ApplicationFiled: December 22, 2014Publication date: May 19, 2016Inventors: Dong Yu, Yu Zhang, Michael L. Seltzer, James G. Droppo
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Publication number: 20140358525Abstract: This document describes various techniques for dual-band speech encoding. In some embodiments, a first type of speech feature is received from a remote entity, an estimate of a second type of speech feature is determined based on the first type of speech feature, the estimate of the second type of speech feature is provided to a speech recognizer, speech-recognition results based on the estimate of the second type of speech feature are received from the speech recognizer, and the speech-recognition results are transmitted to the remote entity.Type: ApplicationFiled: August 14, 2014Publication date: December 4, 2014Inventors: Alejandro Acero, James G. Droppo, III, Michael L. Seltzer
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Patent number: 8818797Abstract: This document describes various techniques for dual-band speech encoding. In some embodiments, a first type of speech feature is received from a remote entity, an estimate of a second type of speech feature is determined based on the first type of speech feature, the estimate of the second type of speech feature is provided to a speech recognizer, speech-recognition results based on the estimate of the second type of speech feature are received from the speech recognizer, and the speech-recognition results are transmitted to the remote entity.Type: GrantFiled: December 23, 2010Date of Patent: August 26, 2014Assignee: Microsoft CorporationInventors: Alejandro Acero, James G. Droppo, III, Michael L. Seltzer
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Patent number: 8615393Abstract: A noise suppressor for altering a speech signal is trained based on a speech recognition system. An objective function can be utilized to adjust parameters of the noise suppressor. The noise suppressor can be used to alter speech signals for the speech recognition system.Type: GrantFiled: November 15, 2006Date of Patent: December 24, 2013Assignee: Microsoft CorporationInventors: Ivan J. Tashev, Alejandro Acero, James G. Droppo
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Patent number: 8532985Abstract: A warped spectral estimate of an original audio signal can be used to encode a representation of a fine estimate of the original signal. The representation of the warped spectral estimate and the representation of the fine estimate can be sent to a speech recognition system. The representation of the warped spectral estimate can be passed to a speech recognition engine, where it may be used for speech recognition. The representation of the warped spectral estimate can also be used along with the representation of the fine estimate to reconstruct a representation of the original audio signal.Type: GrantFiled: December 3, 2010Date of Patent: September 10, 2013Assignee: Microsoft CoporationInventors: Michael L. Seltzer, James G. Droppo, Henrique S. Malvar, Alejandro Acero, Xing Fan
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Patent number: 8379891Abstract: Sound signals to be output from a loudspeaker array are modified by a plurality of filters designed according to an unconstrained optimization procedure to improve overall performance (e.g., power, directivity) of the loudspeaker array. More particularly, respective filters are configured to receive a signal to be output to a plurality of loudspeakers. Upon receiving the signal, the respective filters individually modify the received signal according to the results of the unconstrained optimization procedure and then output the individually modified signals to respective loudspeakers. The unconstrained optimization procedure takes into account manufacturing tolerances and individually enhances the signal output to each of a plurality of individual loudspeakers within an array to achieve an overall improvement in performance.Type: GrantFiled: June 4, 2008Date of Patent: February 19, 2013Assignee: Microsoft CorporationInventors: Ivan J. Tashev, James G. Droppo, Michael L. Seltzer, Alejandro Acero
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Patent number: 8306817Abstract: In an automatic speech recognition system, a feature extractor extracts features from a speech signal, and speech is recognized by the automatic speech recognition system based on the extracted features. Noise reduction as part of the feature extractor is provided by feature enhancement in which feature-domain noise reduction in the form of Mel-frequency cepstra is provided based on the minimum means square error criterion. Specifically, the devised method takes into account the random phase between the clean speech and the mixing noise. The feature-domain noise reduction is performed in a dimension-wise fashion to the individual dimensions of the feature vectors input to the automatic speech recognition system, in order to perform environment-robust speech recognition.Type: GrantFiled: January 8, 2008Date of Patent: November 6, 2012Assignee: Microsoft CorporationInventors: Dong Yu, Alejandro Acero, James G. Droppo, Li Deng
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Publication number: 20120166186Abstract: This document describes various techniques for dual-band speech encoding. In some embodiments, a first type of speech feature is received from a remote entity, an estimate of a second type of speech feature is determined based on the first type of speech feature, the estimate of the second type of speech feature is provided to a speech recognizer, speech-recognition results based on the estimate of the second type of speech feature are received from the speech recognizer, and the speech-recognition results are transmitted to the remote entity.Type: ApplicationFiled: December 23, 2010Publication date: June 28, 2012Applicant: Microsoft CorporationInventors: Alejandro Acero, James G. Droppo, III, Michael L. Seltzer
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Publication number: 20120143599Abstract: A warped spectral estimate of an original audio signal can be used to encode a representation of a fine estimate of the original signal. The representation of the warped spectral estimate and the representation of the fine estimate can be sent to a speech recognition system. The representation of the warped spectral estimate can be passed to a speech recognition engine, where it may be used for speech recognition. The representation of the warped spectral estimate can also be used along with the representation of the fine estimate to reconstruct a representation of the original audio signal.Type: ApplicationFiled: December 3, 2010Publication date: June 7, 2012Applicant: Microsoft CorporationInventors: Michael L. Seltzer, James G. Droppo, Henrique S. Malvar, Alejandro Acero, Xing Fan
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Patent number: 8180636Abstract: Pitch is tracked for individual samples, which are taken much more frequently than an analysis frame. Speech is identified based on the tracked pitch and the speech components of the signal are removed with a time-varying filter, leaving only an estimate of a time-varying speech signal. This estimate is then used to generate a time-varying noise model which, in turn, can be used to enhance speech related systems.Type: GrantFiled: March 7, 2011Date of Patent: May 15, 2012Assignee: Microsoft CorporationInventors: James G. Droppo, Alejandro Acero, Luis Buera
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Publication number: 20110161078Abstract: Pitch is tracked for individual samples, which are taken much more frequently than an analysis frame. Speech is identified based on the tracked pitch and the speech components of the signal are removed with a time-varying filter, leaving only an estimate of a time-varying speech signal. This estimate is then used to generate a time-varying noise model which, in turn, can be used to enhance speech related systems.Type: ApplicationFiled: March 7, 2011Publication date: June 30, 2011Applicant: Microsoft CorporationInventors: James G. Droppo, Alejandro Acero, Luis Buera
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Patent number: 7925502Abstract: Pitch is tracked for individual samples, which are taken much more frequently than an analysis frame. Speech is identified based on the tracked pitch and the speech components of the signal are removed with a time-varying filter, leaving only an estimate of a time-varying speech signal. This estimate is then used to generate a time-varying noise model which, in turn, can be used to enhance speech related systems.Type: GrantFiled: April 19, 2007Date of Patent: April 12, 2011Assignee: Microsoft CorporationInventors: James G. Droppo, Alejandro Acero, Luis Buera
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Patent number: 7885812Abstract: Parameters for a feature extractor and acoustic model of a speech recognition module are trained. An objective function is utilized to determine values for the feature extractor parameters and the acoustic model parameters.Type: GrantFiled: November 15, 2006Date of Patent: February 8, 2011Assignee: Microsoft CorporationInventors: Alejandro Acero, James G. Droppo, Milind V. Mahajan
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Patent number: 7769582Abstract: A method and apparatus are provided for using the uncertainty of a noise-removal process during pattern recognition. In particular, noise is removed from a representation of a portion of a noisy signal to produce a representation of a cleaned signal. In the meantime, an uncertainty associated with the noise removal is computed and is used with the representation of the cleaned signal to modify a probability for a phonetic state in the recognition system. In particular embodiments, the uncertainty is used to modify a probability distribution, by increasing the variance in each Gaussian distribution by the amount equal to the estimated variance of the cleaned signal, which is used in decoding the phonetic state sequence in a pattern recognition task.Type: GrantFiled: July 25, 2008Date of Patent: August 3, 2010Assignee: Microsoft CorporationInventors: James G. Droppo, Alejandro Acero, Li Deng