Patents by Inventor Michael Picheny
Michael Picheny 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: 11094322Abstract: A method, a system, and a computer program product are provided. Speech signals from a medical conversation between a medical provider and a patient are converted to text based on a first domain model associated with a medical scenario. The first domain model is selected from multiple domain models associated with a workflow of the medical provider. One or more triggers are detected, each of which indicates a respective change in the medical scenario. A corresponding second domain model is applied to the medical conversation to more accurately convert the speech signals to text in response to each of the detected one or more triggers. The corresponding second domain model is associated with a respective change in the medical scenario of the workflow of the medical provider. A clinical note is provided based on the text produced by converting the speech signals.Type: GrantFiled: February 7, 2019Date of Patent: August 17, 2021Assignee: International Business Machines CorporationInventors: Andrew J. Lavery, Kenney Ng, Michael Picheny, Paul C. Tang
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Patent number: 10902843Abstract: Audio features, such as perceptual linear prediction (PLP) features and time derivatives thereof, are extracted from frames of training audio data including speech by multiple speakers, and silence, such as by using linear discriminant analysis (LDA). The frames are clustered into k-means clusters using distance measures, such as Mahalanobis distance measures, of means and variances of the extracted audio features of the frames. A recurrent neural network (RNN) is trained on the extracted audio features of the frames and cluster identifiers of the k-means clusters into which the frames have been clustered. The RNN is applied to audio data to segment audio data into segments that each correspond to one of the cluster identifiers. Each segment can be assigned a label corresponding to one of the cluster identifiers. Speech recognition can be performed on the segments.Type: GrantFiled: November 15, 2019Date of Patent: January 26, 2021Assignee: International Business Machines CorporationInventors: Dimitrios B. Dimitriadis, David C. Haws, Michael Picheny, George Saon, Samuel Thomas
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Patent number: 10839792Abstract: A method (and structure and computer product) for learning Out-of-Vocabulary (OOV) words in an Automatic Speech Recognition (ASR) system includes using an Acoustic Word Embedding Recurrent Neural Network (AWE RNN) to receive a character sequence for a new OOV word for the ASR system, the RNN providing an Acoustic Word Embedding (AWE) vector as an output thereof. The AWE vector output from the AWE RNN is provided as an input into an Acoustic Word Embedding-to-Acoustic-to-Word Neural Network (AWE?A2W NN) trained to provide an OOV word weight value from the AWE vector. The OOV word weight is inserted into a listing of Acoustic-to-Word (A2W) word embeddings used by the ASR system to output recognized words from an input of speech acoustic features, wherein the OOV word weight is inserted into the A2W word embeddings list relative to existing weights in the A2W word embeddings list.Type: GrantFiled: February 5, 2019Date of Patent: November 17, 2020Assignees: INTERNATIONAL BUSINESS MACHINES CORPORATION, TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGOInventors: Kartik Audhkhasi, Karen Livescu, Michael Picheny, Shane Settle
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Publication number: 20200258510Abstract: A method, a system, and a computer program product are provided. Speech signals from a medical conversation between a medical provider and a patient are converted to text based on a first domain model associated with a medical scenario. The first domain model is selected from multiple domain models associated with a workflow of the medical provider. One or more triggers are detected, each of which indicates a respective change in the medical scenario. A corresponding second domain model is applied to the medical conversation to more accurately convert the speech signals to text in response to each of the detected one or more triggers. The corresponding second domain model is associated with a respective change in the medical scenario of the workflow of the medical provider. A clinical note is provided based on the text produced by converting the speech signals.Type: ApplicationFiled: February 7, 2019Publication date: August 13, 2020Inventors: Andrew J. Lavery, Kenney Ng, Michael Picheny, Paul C. Tang
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Publication number: 20200251096Abstract: A method (and structure and computer product) for learning Out-of-Vocabulary (OOV) words in an Automatic Speech Recognition (ASR) system includes using an Acoustic Word Embedding Recurrent Neural Network (AWE RNN) to receive a character sequence for a new OOV word for the ASR system, the RNN providing an Acoustic Word Embedding (AWE) vector as an output thereof. The AWE vector output from the AWE RNN is provided as an input into an Acoustic Word Embedding-to-Acoustic-to-Word Neural Network (AWE?A2W NN) trained to provide an OOV word weight value from the AWE vector. The OOV word weight is inserted into a listing of Acoustic-to-Word (A2W) word embeddings used by the ASR system to output recognized words from an input of speech acoustic features, wherein the OOV word weight is inserted into the A2W word embeddings list relative to existing weights in the A2W word embeddings list.Type: ApplicationFiled: February 5, 2019Publication date: August 6, 2020Inventors: Kartik AUDHKHASI, Karen Livescu, Michael Picheny, Shane Settle
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Patent number: 10546575Abstract: Audio features, such as perceptual linear prediction (PLP) features and time derivatives thereof, are extracted from frames of training audio data including speech by multiple speakers, and silence, such as by using linear discriminant analysis (LDA). The frames are clustered into k-means clusters using distance measures, such as Mahalanobis distance measures, of means and variances of the extracted audio features of the frames. A recurrent neural network (RNN) is trained on the extracted audio features of the frames and cluster identifiers of the k-means clusters into which the frames have been clustered. The RNN is applied to audio data to segment audio data into segments that each correspond to one of the cluster identifiers. Each segment can be assigned a label corresponding to one of the cluster identifiers. Speech recognition can be performed on the segments.Type: GrantFiled: December 14, 2016Date of Patent: January 28, 2020Assignee: International Business Machines CorporationInventors: Dimitrios B. Dimitriadis, David C. Haws, Michael Picheny, George Saon, Samuel Thomas
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Patent number: 10249292Abstract: Speaker diarization is performed on audio data including speech by a first speaker, speech by a second speaker, and silence. The speaker diarization includes segmenting the audio data using a long short-term memory (LSTM) recurrent neural network (RNN) to identify change points of the audio data that divide the audio data into segments. The speaker diarization includes assigning a label selected from a group of labels to each segment of the audio data using the LSTM RNN. The group of labels comprising includes labels corresponding to the first speaker, the second speaker, and the silence. Each change point is a transition from one of the first speaker, the second speaker, and the silence to a different one of the first speaker, the second speaker, and the silence. Speech recognition can be performed on the segments that each correspond to one of the first speaker and the second speaker.Type: GrantFiled: December 14, 2016Date of Patent: April 2, 2019Assignee: International Business Machines CorporationInventors: Dimitrios B. Dimitriadis, David C. Haws, Michael Picheny, George Saon, Samuel Thomas
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Publication number: 20180166067Abstract: Audio features, such as perceptual linear prediction (PLP) features and time derivatives thereof, are extracted from frames of training audio data including speech by multiple speakers, and silence, such as by using linear discriminant analysis (LDA). The frames are clustered into k-means clusters using distance measures, such as Mahalanobis distance measures, of means and variances of the extracted audio features of the frames. A recurrent neural network (RNN) is trained on the extracted audio features of the frames and cluster identifiers of the k-means clusters into which the frames have been clustered. The RNN is applied to audio data to segment audio data into segments that each correspond to one of the cluster identifiers. Each segment can be assigned a label corresponding to one of the cluster identifiers. Speech recognition can be performed on the segments.Type: ApplicationFiled: December 14, 2016Publication date: June 14, 2018Inventors: Dimitrios B. Dimitriadis, David C. Haws, Michael Picheny, George Saon, Samuel Thomas
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Publication number: 20180166066Abstract: Speaker diarization is performed on audio data including speech by a first speaker, speech by a second speaker, and silence. The speaker diarization includes segmenting the audio data using a long short-term memory (LSTM) recurrent neural network (RNN) to identify change points of the audio data that divide the audio data into segments. The speaker diarization includes assigning a label selected from a group of labels to each segment of the audio data using the LSTM RNN. The group of labels comprising includes labels corresponding to the first speaker, the second speaker, and the silence. Each change point is a transition from one of the first speaker, the second speaker, and the silence to a different one of the first speaker, the second speaker, and the silence. Speech recognition can be performed on the segments that each correspond to one of the first speaker and the second speaker.Type: ApplicationFiled: December 14, 2016Publication date: June 14, 2018Inventors: Dimitrios B. Dimitriadis, David C. Haws, Michael Picheny, George Saon, Samuel Thomas
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Patent number: 8924210Abstract: Techniques for converting spoken speech into written speech are provided. The techniques include transcribing input speech via speech recognition, mapping each spoken utterance from input speech into a corresponding formal utterance, and mapping each formal utterance into a stylistically formatted written utterance.Type: GrantFiled: May 28, 2014Date of Patent: December 30, 2014Assignee: Nuance Communications, Inc.Inventors: Sara H. Basson, Rick Hamilton, Dan Ning Jiang, Dimitri Kanevsky, David Nahamoo, Michael Picheny, Bhuvana Ramabhadran, Tara N. Sainath
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Patent number: 8856004Abstract: Techniques for converting spoken speech into written speech are provided. The techniques include transcribing input speech via speech recognition, mapping each spoken utterance from input speech into a corresponding formal utterance, and mapping each formal utterance into a stylistically formatted written utterance.Type: GrantFiled: May 13, 2011Date of Patent: October 7, 2014Assignee: Nuance Communications, Inc.Inventors: Sara H. Basson, Rick Hamilton, Dan Ning Jiang, Dimitri Kanevsky, David Nahamoo, Michael Picheny, Bhuvana Ramabhadran, Tara N. Sainath
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Publication number: 20140278410Abstract: Techniques for converting spoken speech into written speech are provided. The techniques include transcribing input speech via speech recognition, mapping each spoken utterance from input speech into a corresponding formal utterance, and mapping each formal utterance into a stylistically formatted written utterance.Type: ApplicationFiled: May 28, 2014Publication date: September 18, 2014Applicant: Nuance Communications, Inc.Inventors: Sara H. Basson, Rick Hamilton, Dan Ning Jiang, Dimitri Kanevsky, David Nahamoo, Michael Picheny, Bhuvana Ramabhadran, Tara N. Sainath
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Publication number: 20120290299Abstract: Techniques for converting spoken speech into written speech are provided. The techniques include transcribing input speech via speech recognition, mapping each spoken utterance from input speech into a corresponding formal utterance, and mapping each formal utterance into a stylistically formatted written utterance.Type: ApplicationFiled: May 13, 2011Publication date: November 15, 2012Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Sara H. Basson, Rick Hamilton, Dan Ning Jiang, Dimitri Kanevsky, David Nahamoo, Michael Picheny, Bhuvana Ramabhadran, Tara N. Sainath
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Publication number: 20060229873Abstract: A technique for producing speech output in an automatic dialog system is provided. Communication is received from a user at the automatic dialog system. A context of the communication from the user is detected in a context detector of the automatic dialog system. A message is provided to the user from a text-to-speech system of the automatic dialog system in communication with the context detector, wherein the message is provided in accordance with the detected context of the communication.Type: ApplicationFiled: March 29, 2005Publication date: October 12, 2006Applicant: International Business Machines CorporationInventors: Ellen Eide, Wael Hamza, Michael Picheny
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Publication number: 20060229876Abstract: A method, apparatus and a computer program product to generate an audible speech word that corresponds to text. The method includes providing a text word and, in response to the text word, processing pre-recorded speech segments that are derived from a plurality of speakers to selectively concatenate together speech segments based on at least one cost function to form audio data for generating an audible speech word that corresponds to the text word. A data structure is also provided for use in a concatenative text-to-speech system that includes a plurality of speech segments derived from a plurality of speakers, where each speech segment includes an associated attribute vector each of which is comprised of at least one attribute vector element that identifies the speaker from which the speech segment was derived.Type: ApplicationFiled: April 7, 2005Publication date: October 12, 2006Inventors: Andrew Aaron, Ellen Eide, Wael Hamza, Michael Picheny, Charles Rutherfoord, Zhi Shuang, Maria Smith
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Publication number: 20060074634Abstract: A method, apparatus and computer instructions is provided for fast semi-automatic semantic annotation. Given a limited annotated corpus, the present invention assigns a tag and a label to each word of the next limited annotated corpus using a parser engine, a similarity engine, and a SVM engine. A rover then combines the parse trees from the three engines and annotates the next chunk of limited annotated corpus with confidence, such that the efforts required for human annotation is reduced.Type: ApplicationFiled: October 6, 2004Publication date: April 6, 2006Applicant: International Business Machines CorporationInventors: Yuqing Gao, Michael Picheny, Ruhi Sarikaya
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Publication number: 20050119885Abstract: In a speech recognition system, the combination of a log-linear model with a multitude of speech features is provided to recognize unknown speech utterances. The speech recognition system models the posterior probability of linguistic units relevant to speech recognition using a log-linear model. The posterior model captures the probability of the linguistic unit given the observed speech features and the parameters of the posterior model. The posterior model may be determined using the probability of the word sequence hypotheses given a multitude of speech features. Log-linear models are used with features derived from sparse or incomplete data. The speech features that are utilized may include asynchronous, overlapping, and statistically non-independent speech features. Not all features used in training need to appear in testing/recognition.Type: ApplicationFiled: November 28, 2003Publication date: June 2, 2005Inventors: Scott Axelrod, Sreeram Balakrishnan, Stanley Chen, Yuging Gao, Ramesh Gopinath, Hong-Kwang Kuo, Benoit Maison, David Nahamoo, Michael Picheny, George Saon, Geoffrey Zweig
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Publication number: 20050055209Abstract: A system and method for speech recognition includes generating a set of likely hypotheses in recognizing speech, rescoring the likely hypotheses by using semantic content by employing semantic structured language models, and scoring parse trees to identify a best sentence according to the sentence's parse tree by employing the semantic structured language models to clarify the recognized speech.Type: ApplicationFiled: September 5, 2003Publication date: March 10, 2005Inventors: Mark Epstein, Hakan Erdogan, Yuqing Gao, Michael Picheny, Ruhi Sarikaya
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Patent number: 6859778Abstract: A multi-lingual translation system that provides multiple output sentences for a given word or phrase. Each output sentence for a given word or phrase reflects, for example, a different emotional emphasis, dialect, accents, loudness or rates of speech. A given output sentence could be selected automatically, or manually as desired, to create a desired effect. For example, the same output sentence for a given word or phrase can be recorded three times, to selectively reflect excitement, sadness or fear. The multi-lingual translation system includes a phrase-spotting mechanism, a translation mechanism, a speech output mechanism and optionally, a language understanding mechanism or an event measuring mechanism or both. The phrase-spotting mechanism identifies a spoken phrase from a restricted domain of phrases. The language understanding mechanism, if present, maps the identified phrase onto a small set of formal phrases.Type: GrantFiled: March 16, 2000Date of Patent: February 22, 2005Assignees: International Business Machines Corporation, OIPENN, Inc.Inventors: Raimo Bakis, Mark Edward Epstein, William Stuart Meisel, Miroslav Novak, Michael Picheny, Ridley M. Whitaker
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Patent number: 6556972Abstract: A multi-lingual time-synchronized translation system and method provide automatic time-synchronized spoken translations of spoken phrases. The multi-lingual time-synchronized translation system includes a phrase-spotting mechanism, optionally, a language understanding mechanism, a translation mechanism, a speech output mechanism and an event measuring mechanism. The phrase-spotting mechanism identifies a spoken phrase from a restricted domain of phrases. The language understanding mechanism, if present, maps the identified phrase onto a small set of formal phrases. The translation mechanism maps the formal phrase onto a well-formed phrase in one or more target languages. The speech output mechanism produces high-quality output speech using the output of the event measuring mechanism for time synchronization. The event-measuring mechanism measures the duration of various key events in the source phrase.Type: GrantFiled: March 16, 2000Date of Patent: April 29, 2003Assignee: International Business Machines CorporationInventors: Raimo Bakis, Mark Edward Epstein, William Stuart Meisel, Miroslav Novak, Michael Picheny, Ridley M. Whitaker