Amazon Technologies, Inc.

Michael James Rodehorst

Alexander David Rosen

Shiv Naga Prasad Vitaladevuni

Gengshen Fu

Naresh Narayanan

Thibaud Senechal

Daniel Robert Rashid

Aaron Lee Mathers Challenner

Colin Wills Wightman

Varun Kumar Nagaraja

George Jay Tucker

Joseph Wang

Rajath Kumar Mysore Pradeep Kumar

A system and method performs speech command verification to determine if audio data includes a representation of a speech command. A first neural network may process portions of the audio data before and after a representation of a wake trigger in the audio data. A second neural network may process the audio data using a recurrent neural network to determine if the audio data includes a representation of a wake trigger.

Speech command verification

A system and method performs wakeword detection using a neural network model that includes a recurrent neural network (RNN) for processing variable-length wakewords. To prevent the model from being influenced by non-wakeword speech, multiple instances of the model are created to process audio data, and each instance is configured to use weights determined by training data. The model may instead or in addition be used to process the audio data only when a likelihood that the audio data corresponds to the wakeword is greater than a threshold. The model may process the audio data as represented by groups of acoustic feature vectors; computations for feature vectors common to different groups may be re-used.

Speech processing using a recurrent neural network

Systems and methods for generating sound profiles of artificial commands detected by multiple voice activated electronic devices is described herein. In some embodiments, numerous voice activated electronic devices may send audio data representing a phrase to a backend system at a substantially same time. Text data representing the phrase, and counts for instances of that text data, may be generated. If the number of counts exceeds a predefined threshold, the backend system may cause any remaining response generation functionality that particular command that is in excess of the predefined threshold to be stopped, and those devices returned to a sleep state. In some embodiments, a sound profile unique to the phrase that caused the excess of the predefined threshold may be generated such that future instances of the same phrase may be recognized prior to text data being generated, conserving the backend system's resources.

Sound profile generation based on speech recognition results exceeding a threshold

Systems and methods for detecting similar audio being received by separate voice activated electronic devices, and ignoring those commands, is described herein. In some embodiments, a voice activated electronic device may be activated by a wakeword that is output by the additional electronic device, such as a television or radio, may capture audio of sound subsequently following the wakeword, and may send audio data representing the sound to a backend system. Upon receipt, the backend system may, in parallel to performing automated speech recognition processing to the audio data, generate a sound profile of the audio data, and may compare that sound profile to sound profiles of recently received audio data and/or flagged sound profiles. If the generated sound profile is determined to match another sound profiles, then the automated speech recognition processing may be stopped, and the voice activated electronic device may be instructed to return to a keyword spotting mode. If the matching sound profile is not already stored in a database of known sound profiles, it can be stored for future comparisons.

Methods and devices for ignoring similar audio being received by a system

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Belmont, MA, United States of America

Michael James Rodehorst