Google Inc.

Justin Lu

Victor Carbune

Nicolo D'Ercole

Matthew Nirvan Sharifi

Behshad Behzadi

Zaheed Sabur

Michael Golikov

Luv Kothari

Julia Proskurnia

Jacopo Sannazzaro Natta

Ondrej Skopek

Daniel Valcarce

Mohamad Hassan Rom

Krishna Sapkota

Magali Boizot-Roche

Kevin I Kilgour

Kevin Kilgour

Márius Sajgalík

Implementations described herein relate to an application and/or automated assistant that can identify arrangement operations to perform for arranging text during speech-to-text operations—without a user having to expressly identify the arrangement operations. In some instances, a user that is dictating a document (e.g., an email, a text message, etc.) can provide a spoken utterance to an application in order to incorporate textual content. However, in some of these instances, certain corresponding arrangements are needed for the textual content in the document. The textual content that is derived from the spoken utterance can be arranged by the application based on an intent, vocalization features, and/or contextual features associated with the spoken utterance and/or a type of the application associated with the document, without the user expressly identifying the corresponding arrangements. In this way, the application can infer content arrangement operations from a spoken utterance that only specifies the textual content.

Arranging and/or clearing speech-to-text content without a user providing express instructions

Some implementations process, using warm word model(s), a stream of audio data to determine a portion of the audio data that corresponds to particular word(s) and/or phrase(s) (e.g., a warm word) associated with an assistant command, process, using an automatic speech recognition (ASR) model, a preamble portion of the audio data (e.g., that precedes the warm word) and/or a postamble portion of the audio data (e.g., that follows the warm word) to generate ASR output, and determine, based on processing the ASR output, whether a user intended the assistant command to be performed. Additional or alternative implementations can process the stream of audio data using a speaker identification (SID) model to determine whether the audio data is sufficient to identify the user that provided a spoken utterance captured in the stream of audio data, and determine if that user is authorized to cause performance of the assistant command.

Contextual suppression of assistant command(s)

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Zurich, Switzerland

Justin Lu