View source for TiVo Corporation (20240347053). GENERATING TOPIC-SPECIFIC LANGUAGE MODELS simplified abstract

=GENERATING TOPIC-SPECIFIC LANGUAGE MODELS=


==Organization Name==

[[:Category:TiVo Corporation|TiVo Corporation]]


[[Category:TiVo Corporation]]

==Inventor(s)==

[[:Category:David F. Houghton of Brattleboro VT (US)|David F. Houghton of Brattleboro VT (US)]][[Category:David F. Houghton of Brattleboro VT (US)]]

[[:Category:Seth Michael Murray of Redwood City CA (US)|Seth Michael Murray of Redwood City CA (US)]][[Category:Seth Michael Murray of Redwood City CA (US)]]

[[:Category:Sibley Verbeck Simon of Santa Cruz CA (US)|Sibley Verbeck Simon of Santa Cruz CA (US)]][[Category:Sibley Verbeck Simon of Santa Cruz CA (US)]]

==GENERATING TOPIC-SPECIFIC LANGUAGE MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240347053 titled 'GENERATING TOPIC-SPECIFIC LANGUAGE MODELS

==Simplified Explanation==
The patent application discusses improving speech recognition by creating a topic-specific language model based on the content of the audio signal.

==Key Features and Innovation==
* Initial pass on audio signal using a generic language model
* Determining topics based on identified words
* Retrieving a corpus of text related to those topics
* Creating a topic-specific language model using the retrieved text
* Adapting the generic language model based on the retrieved corpus

==Potential Applications==
This technology can be applied in various fields such as:
* Voice-controlled devices
* Transcription services
* Language translation tools

==Problems Solved==
* Enhances accuracy of speech recognition
* Improves understanding of context in audio signals
* Enables better communication with voice-activated systems

==Benefits==
* Increased efficiency in speech recognition
* Enhanced user experience with voice-controlled devices
* Improved accuracy in transcribing audio content

==Commercial Applications==
* This technology can be utilized in developing advanced speech recognition systems for commercial use, such as in customer service centers, transcription services, and language learning platforms.

==Prior Art==
Prior art related to this technology may include research on topic modeling in natural language processing and speech recognition systems.

==Frequently Updated Research==
Stay updated on advancements in natural language processing, speech recognition, and topic modeling to enhance the capabilities of this technology.

==Questions about Speech Recognition Technology==
===How does a topic-specific language model improve speech recognition accuracy?===
A topic-specific language model helps the system better understand the context of the audio signal, leading to more accurate transcription and interpretation.

===What are the potential challenges in implementing a topic-specific language model in speech recognition devices?===
Implementing a topic-specific language model may require significant computational resources and a large corpus of text data for training, which can be a challenge for some systems.


==Original Abstract Submitted==

speech recognition may be improved by generating and using a topic specific language model. a topic specific language model may be created by performing an initial pass on an audio signal using a generic or basis language model. a speech recognition device may then determine topics relating to the audio signal based on the words identified in the initial pass and retrieve a corpus of text relating to those topics. using the retrieved corpus of text, the speech recognition device may create a topic specific language model. in one example, the speech recognition device may adapt or otherwise modify the generic language model based on the retrieved corpus of text.



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