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US Patent 10529320 Complex evolution recurrent neural networks

Patent 10529320 was granted and assigned to Google on January, 2020 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Google
Google
Current Assignee
Google
Google
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10529320
Date of Patent
January 7, 2020
Patent Application Number
16251430
Date Filed
January 18, 2019
Patent Citations
‌
US Patent 10140980 Complex linear projection for acoustic modeling
Patent Citations Received
0
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US Patent 11443748 Metric learning of speaker diarization
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US Patent 11863221 Low size, weight and power (swap) efficient hardware implementation of a wide instantaneous bandwidth neuromorphic adaptive core (NeurACore)
0
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US Patent 11886813 Efficient automatic punctuation with robust inference
0
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US Patent 10832680 Speech-to-text engine customization
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US Patent 10930270 Processing audio waveforms
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US Patent 11023580 Systems and methods for cross-product malware categorization
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US Patent 11069344 Complex evolution recurrent neural networks
...
Patent Primary Examiner
‌
Douglas Godbold
Patent abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using complex evolution recurrent neural networks. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A first vector sequence comprising audio features determined from the audio data is generated. A second vector sequence is generated, as output of a first recurrent neural network in response to receiving the first vector sequence as input, where the first recurrent neural network has a transition matrix that implements a cascade of linear operators comprising (i) first linear operators that are complex-valued and unitary, and (ii) one or more second linear operators that are non-unitary. An output vector sequence of a second recurrent neural network is generated. A transcription for the utterance is generated based on the output vector sequence generated by the second recurrent neural network. The transcription for the utterance is provided.

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