Patents by Inventor Monika Woszczyna
Monika Woszczyna 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: 20210193152Abstract: A computer system automatically authenticates a user to a server in response to determining that an audio signal received from one microphone positively correlates with an audio signal received from another microphone that is associated with a computing device at which the user is already authenticated to the server. Two audio signals are received from distinct microphones associated with first and second computing devices. A correlation module performs correlation on the two audio signals. An authentication module automatically authenticates a user to a server at the (first computing device if it is determined that the first audio signal positively correlates with the second audio signal and the user is already authenticated to the server at the second computing device.Type: ApplicationFiled: July 3, 2019Publication date: June 24, 2021Inventor: Monika Woszczyna
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Patent number: 9552809Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: GrantFiled: March 10, 2016Date of Patent: January 24, 2017Assignee: MModal IP LLCInventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Publication number: 20160196821Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: ApplicationFiled: March 10, 2016Publication date: July 7, 2016Applicant: MModal IP LLCInventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Patent number: 9286896Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: GrantFiled: May 16, 2014Date of Patent: March 15, 2016Assignee: MModal IP LLCInventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Publication number: 20140249818Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: ApplicationFiled: May 16, 2014Publication date: September 4, 2014Applicant: MModal IP LLCInventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Patent number: 8731920Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system my identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: GrantFiled: November 30, 2012Date of Patent: May 20, 2014Assignee: MModal IP LLCInventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Publication number: 20130304453Abstract: Techniques are disclosed for automatically generating structured documents based on speech, including identification of relevant concepts and their interpretation. In one embodiment, a structured document generator uses an integrated process to generate a structured textual document (such as a structured textual medical report) based on a spoken audio stream. The spoken audio stream may be recognized using a language model which includes a plurality of sub-models arranged in a hierarchical structure. Each of the sub-models may correspond to a concept that is expected to appear in the spoken audio stream. Different portions of the spoken audio stream may be recognized using different sub-models. The resulting structured textual document may have a hierarchical structure that corresponds to the hierarchical structure of the language sub-models that were used to generate the structured textual document.Type: ApplicationFiled: May 22, 2009Publication date: November 14, 2013Inventors: Juergen Fritsch, Michael Finke, Detlef Koll, Monika Woszczyna, Girija Yegnanarayanan
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Patent number: 8335688Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: GrantFiled: August 20, 2004Date of Patent: December 18, 2012Assignee: Multimodal Technologies, LLCInventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Publication number: 20100299135Abstract: Techniques are disclosed for automatically generating structured documents based on speech, including identification of relevant concepts and their interpretation. In one embodiment, a structured document generator uses an integrated process to generate a structured textual document (such as a structured textual medical report) based on a spoken audio stream. The spoken audio stream may be recognized using a language model which includes a plurality of sub-models arranged in a hierarchical structure. Each of the sub-models may correspond to a concept that is expected to appear in the spoken audio stream. Different portions of the spoken audio stream may be recognized using different sub-models. The resulting structured textual document may have a hierarchical structure that corresponds to the hierarchical structure of the language sub-models that were used to generate the structured textual document.Type: ApplicationFiled: May 22, 2009Publication date: November 25, 2010Inventors: Juergen Fritsch, Michael Finke, Detlef Koll, Monika Woszczyna, Girija Yegnanarayanan
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Patent number: 7584103Abstract: Techniques are disclosed for automatically generating structured documents based on speech, including identification of relevant concepts and their interpretation. In one embodiment, a structured document generator uses an integrated process to generate a structured textual document (such as a structured textual medical report) based on a spoken audio stream. The spoken audio stream may be recognized using a language model which includes a plurality of sub-models arranged in a hierarchical structure. Each of the sub-models may correspond to a concept that is expected to appear in the spoken audio stream. Different portions of the spoken audio stream may be recognized using different sub-models. The resulting structured textual document may have a hierarchical structure that corresponds to the hierarchical structure of the language sub-models that were used to generate the structured textual document.Type: GrantFiled: August 20, 2004Date of Patent: September 1, 2009Assignee: Multimodal Technologies, Inc.Inventors: Juergen Fritsch, Michael Finke, Detlef Koll, Monika Woszczyna, Girija Yegnanarayanan
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Publication number: 20090048833Abstract: Techniques are disclosed for automatically generating structured documents based on speech, including identification of relevant concepts and their interpretation. In one embodiment, a structured document generator uses an integrated process to generate a structured textual document (such as a structured textual medical report) based on a spoken audio stream. The spoken audio stream may be recognized using a language model which includes a plurality of sub-models arranged in a hierarchical structure. Each of the sub-models may correspond to a concept that is expected to appear in the spoken audio stream. Different portions of the spoken audio stream may be recognized using different sub-models. The resulting structured textual document may have a hierarchical structure that corresponds to the hierarchical structure of the language sub-models that were used to generate the structured textual document.Type: ApplicationFiled: October 17, 2008Publication date: February 19, 2009Inventors: Juergen Fritsch, Michael Finke, Detlef Koll, Monika Woszczyna, Girija Yegnanarayanan
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Publication number: 20060041427Abstract: A system is provided for training an acoustic model for use in speech recognition. In particular, such a system may be used to perform training based on a spoken audio stream and a non-literal transcript of the spoken audio stream. Such a system may identify text in the non-literal transcript which represents concepts having multiple spoken forms. The system may attempt to identify the actual spoken form in the audio stream which produced the corresponding text in the non-literal transcript, and thereby produce a revised transcript which more accurately represents the spoken audio stream. The revised, and more accurate, transcript may be used to train the acoustic model, thereby producing a better acoustic model than that which would be produced using conventional techniques, which perform training based directly on the original non-literal transcript.Type: ApplicationFiled: August 20, 2004Publication date: February 23, 2006Inventors: Girija Yegnanarayanan, Michael Finke, Juergen Fritsch, Detlef Koll, Monika Woszczyna
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Publication number: 20060041428Abstract: Techniques are disclosed for automatically generating structured documents based on speech, including identification of relevant concepts and their interpretation. In one embodiment, a structured document generator uses an integrated process to generate a structured textual document (such as a structured textual medical report) based on a spoken audio stream. The spoken audio stream may be recognized using a language model which includes a plurality of sub-models arranged in a hierarchical structure. Each of the sub-models may correspond to a concept that is expected to appear in the spoken audio stream. Different portions of the spoken audio stream may be recognized using different sub-models. The resulting structured textual document may have a hierarchical structure that corresponds to the hierarchical structure of the language sub-models that were used to generate the structured textual document.Type: ApplicationFiled: August 20, 2004Publication date: February 23, 2006Inventors: Juergen Fritsch, Michael Finke, Detlef Koll, Monika Woszczyna, Girija Yegnanarayanan