View source for 18486145. SCALABLE DYNAMIC CLASS LANGUAGE MODELING simplified abstract (GOOGLE LLC)

=SCALABLE DYNAMIC CLASS LANGUAGE MODELING=


==Organization Name==

[[:Category:GOOGLE LLC|GOOGLE LLC]]


[[Category:GOOGLE LLC]]

==Inventor(s)==

[[:Category:Justin Max Scheiner of New York NY (US)|Justin Max Scheiner of New York NY (US)]][[Category:Justin Max Scheiner of New York NY (US)]]

[[:Category:Petar Aleksic of Jersey City NJ (US)|Petar Aleksic of Jersey City NJ (US)]][[Category:Petar Aleksic of Jersey City NJ (US)]]

==SCALABLE DYNAMIC CLASS LANGUAGE MODELING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18486145 titled 'SCALABLE DYNAMIC CLASS LANGUAGE MODELING

==Simplified Explanation==

- Systems and methods for dynamically adapting speech recognition for individual voice queries of a user using class-based language models.
- Receiving a voice query from a user with audio data and context data.
- Generating one or more class models to identify terms based on context data and assign a class to each term.
- Accessing and processing a language model with a residual unigram for each class to insert a class symbol.
- Generating a transcription of the user's utterance using the modified language model.

== Potential Applications ==
- Improved speech recognition systems for personalized user experiences.
- Enhanced voice-controlled devices and virtual assistants.
- Better accuracy in transcribing user voice queries.

== Problems Solved ==
- Addressing the challenge of accurately recognizing individual voice queries in various contexts.
- Improving the performance of speech recognition systems for diverse users.
- Enhancing the user experience by adapting to specific user preferences and contexts.

== Benefits ==
- Increased accuracy in speech recognition for personalized user interactions.
- Enhanced user satisfaction with voice-controlled devices and services.
- Improved efficiency in transcribing user voice queries.


==Original Abstract Submitted==

This document generally describes systems and methods for dynamically adapting speech recognition for individual voice queries of a user using class-based language models. The method may include receiving a voice query from a user that includes audio data corresponding to an utterance of the user, and context data associated with the user. One or more class models are then generated that collectively identify a first set of terms determined based on the context data, and a respective class to which the respective term is assigned for each respective term in the first set of terms. A language model that includes a residual unigram may then be accessed and processed for each respective class to insert a respective class symbol at each instance of the residual unigram that occurs within the language model. A transcription of the utterance of the user is then generated using the modified language model.



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