US 12,322,388 B2
Correcting speech recognition errors by considering prior user edits and/or assessing fulfillment data
Ágoston Weisz, Zurich (CH); Miroslaw Michalski, Pfaffikon SZ (CH); and Aurélien Boffy, Basel (CH)
Assigned to GOOGLE LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Jun. 16, 2022, as Appl. No. 17/842,166.
Claims priority of provisional application 63/351,664, filed on Jun. 13, 2022.
Prior Publication US 2023/0402034 A1, Dec. 14, 2023
Int. Cl. G10L 15/22 (2006.01); G10L 15/06 (2013.01); G10L 15/18 (2013.01)
CPC G10L 15/22 (2013.01) [G10L 15/063 (2013.01); G10L 15/1815 (2013.01); G10L 2015/223 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method implemented by one or more processors, the method comprising:
processing audio data that captures a spoken utterance provided by a user to an automated assistant that is accessible via a computing device,
wherein the spoken utterance embodies a request to cause the automated assistant to perform one or more operations in furtherance of fulfilling the request;
generating, based on processing the audio data, speech recognition hypothesis data for the spoken utterance,
wherein the speech recognition hypothesis data characterizes a speech recognition hypothesis and an alternate speech recognition hypothesis that are each a corresponding prediction of the spoken utterance,
wherein the alternate speech recognition hypothesis includes one or more correcting terms that are not included in the speech recognition hypothesis, the speech recognition hypothesis includes one or more corrected terms that are not in the alternate speech recognition hypothesis, and
wherein the one or more correcting terms are included, in the alternate speech recognition hypothesis in lieu of the corrected terms, based on one or more prior instance of correction of the one or more corrected terms to the one or more correcting terms;
determining, based on fulfillment data, whether to cause the automated assistant to respond to the speech recognition hypothesis or to instead respond to the alternate speech recognition hypothesis,
wherein the fulfillment data indicates responsiveness of the automated assistant to the speech recognition hypothesis and/or to the alternate speech recognition hypothesis; and
causing, based on the determining, the automated assistant to respond to either the speech recognition hypothesis or the alternate speech recognition hypothesis.