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US Patent 8442821 Multi-frame prediction for hybrid neural network/hidden Markov models

Patent 8442821 was granted and assigned to Google on May, 2013 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
1

Patent attributes

Current Assignee
Google
Google
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
84428211
Patent Inventor Names
Vincent Vanhoucke1
Date of Patent
May 14, 2013
1
Patent Application Number
135607061
Date Filed
July 27, 2012
1
Patent Citations Received
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US Patent 12136419 Multimodality in digital assistant systems
2
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US Patent 12067985 Virtual assistant operations in multi-device environments
3
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US Patent 12067990 Intelligent assistant for home automation
4
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US Patent 12073147 Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
5
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US Patent 12118999 Reducing the need for manual start/end-pointing and trigger phrases
6
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US Patent 11657820 Intelligent digital assistant in a multi-tasking environment
7
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US Patent 11671920 Method and system for operating a multifunction portable electronic device using voice-activation
8
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US Patent 11670289 Multi-command single utterance input method
9
...
Patent Primary Examiner
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Abul Azad
1
Patent abstract

A method and system for multi-frame prediction in a hybrid neural network/hidden Markov model automatic speech recognition (ASR) system is disclosed. An audio input signal may be transformed into a time sequence of feature vectors, each corresponding to respective temporal frame of a sequence of periodic temporal frames of the audio input signal. The time sequence of feature vectors may be concurrently input to a neural network, which may process them concurrently. In particular, the neural network may concurrently determine for the time sequence of feature vectors a set of emission probabilities for a plurality of hidden Markov models of the ASR system, where the set of emission probabilities are associated with the temporal frames. The set of emission probabilities may then be concurrently applied to the hidden Markov models for determining speech content of the audio input signal.

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