View source for Google llc (20240112673). IDENTIFYING AND CORRECTING AUTOMATIC SPEECH RECOGNITION (ASR) MISRECOGNITIONS IN A DECENTRALIZED MANNER simplified abstract

=IDENTIFYING AND CORRECTING AUTOMATIC SPEECH RECOGNITION (ASR) MISRECOGNITIONS IN A DECENTRALIZED MANNER=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Rajiv Mathews of Sunnyvale CA (US)|Rajiv Mathews of Sunnyvale CA (US)]][[Category:Rajiv Mathews of Sunnyvale CA (US)]]

[[:Category:Rohit Prabhavalkar of Santa Clara CA (US)|Rohit Prabhavalkar of Santa Clara CA (US)]][[Category:Rohit Prabhavalkar of Santa Clara CA (US)]]

[[:Category:Giovanni Motta of San Jose CA (US)|Giovanni Motta of San Jose CA (US)]][[Category:Giovanni Motta of San Jose CA (US)]]

[[:Category:Mingqing Chen of Saratoga CA (US)|Mingqing Chen of Saratoga CA (US)]][[Category:Mingqing Chen of Saratoga CA (US)]]

[[:Category:Lillian Zhou of Mountain View CA (US)|Lillian Zhou of Mountain View CA (US)]][[Category:Lillian Zhou of Mountain View CA (US)]]

[[:Category:Dhruv Guliani of San Francisco CA (US)|Dhruv Guliani of San Francisco CA (US)]][[Category:Dhruv Guliani of San Francisco CA (US)]]

[[:Category:Harry Zhang of Sunnyvale CA (US)|Harry Zhang of Sunnyvale CA (US)]][[Category:Harry Zhang of Sunnyvale CA (US)]]

[[:Category:Trevor Strohman of Sunnyvale CA (US)|Trevor Strohman of Sunnyvale CA (US)]][[Category:Trevor Strohman of Sunnyvale CA (US)]]

[[:Category:Françoise Beaufays of Mountain View CA (US)|Françoise Beaufays of Mountain View CA (US)]][[Category:Françoise Beaufays of Mountain View CA (US)]]

==IDENTIFYING AND CORRECTING AUTOMATIC SPEECH RECOGNITION (ASR) MISRECOGNITIONS IN A DECENTRALIZED MANNER - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240112673 titled 'IDENTIFYING AND CORRECTING AUTOMATIC SPEECH RECOGNITION (ASR) MISRECOGNITIONS IN A DECENTRALIZED MANNER

==Simplified Explanation==

The patent application describes a system for identifying and correcting misrecognitions in automatic speech recognition (ASR) systems. Here is a simplified explanation of the abstract:

* On-device processors of a client device predict textual segments corresponding to user utterances and store them as candidate correction pairs.
* The on-device processors transmit these pairs to a remote system, which determines actual correction pairs and updates a global ASR model.
* The global ASR model is distributed to client devices for improved speech recognition accuracy.

== Potential Applications ==
This technology could be applied in:
* Improving the accuracy of voice assistants
* Enhancing transcription services

== Problems Solved ==
* Correcting misrecognitions in ASR systems
* Updating ASR models for better performance

== Benefits ==
* Enhanced user experience with accurate speech recognition
* Increased efficiency in transcribing spoken language

== Potential Commercial Applications ==
* Voice-controlled devices
* Transcription software services

== Possible Prior Art ==
One possible prior art could be the use of machine learning algorithms to improve ASR accuracy.

=== Unanswered Questions ===

=== How does this technology handle different languages or accents in speech recognition? ===
The patent application does not specifically address how the system adapts to various languages or accents in speech recognition.

=== What measures are in place to ensure data privacy and security when transmitting textual segments to remote systems? ===
The patent application does not detail the security protocols in place for transmitting sensitive textual data to remote systems.


==Original Abstract Submitted==

implementations described herein identify and correct automatic speech recognition (asr) misrecognitions. for example, on-device processor(s) of a client device may generate a predicted textual segment that is predicted to correspond to spoken utterance of a user of the client device, and may receive further input that modifies the predicted textual segment to an alternate textual segment. further, the on-device processor(s) may store these textual segments in on-device storage as a candidate correction pair, and transmit the candidate correction pair to a remote system. moreover, remote processor(s) of the remote system may determine that the candidate correction pair is an actual correction pair, and may cause client devices to generate updates for a global asr model for the candidate correction pair. additionally, the remote processor(s) may distribute the global asr model to the client devices and/or additional client devices.



[[Category:G10L15/197]]
[[Category:G10L15/06]]
[[Category:G10L15/22]]
[[Category:G10L15/30]]