Google llc (20240112672). GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S) simplified abstract
Contents
- 1 GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S) - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S)
Organization Name
Inventor(s)
Rajiv Mathews of Sunnyvale CA (US)
Dragan Zivkovic of Sunnyvale CA (US)
Khe Chai Sim of Dublin CA (US)
GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S) - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240112672 titled 'GENERATION AND UTILIZATION OF PSEUDO-CORRECTION(S) TO PREVENT FORGETTING OF PERSONALIZED ON-DEVICE AUTOMATIC SPEECH RECOGNITION (ASR) MODEL(S)
Simplified Explanation
The abstract of the patent application describes a system where on-device processors store corrections to automatic speech recognition (ASR) processing in on-device storage, along with personalized ASR models based on these corrections.
- On-device processors store corrections to ASR processing in on-device storage with a time to live (TTL).
- Corrections include modified speech hypotheses.
- On-device processors personalize ASR models based on these corrections.
- Pseudo-corrections are stored in on-device storage with an additional TTL.
- Personalization of ASR models is done based on pseudo-corrections to prevent forgetting.
Potential Applications
The technology described in the patent application could be applied in various fields such as:
- Speech recognition software development
- Personalized digital assistants
- Language learning applications
Problems Solved
The technology addresses the following issues:
- Improving the accuracy of ASR systems
- Personalizing ASR models for individual users
- Preventing forgetting of corrections in ASR processing
Benefits
The benefits of this technology include:
- Enhanced user experience with ASR systems
- Increased accuracy in speech recognition
- Customized ASR models for improved performance
Potential Commercial Applications
The technology could be utilized in commercial applications such as:
- Voice-controlled devices
- Customer service chatbots
- Transcription services
Possible Prior Art
One possible prior art for this technology could be the use of machine learning algorithms to personalize ASR models based on user corrections.
Unanswered Questions
How does the system handle multiple corrections for the same speech hypothesis?
The abstract does not specify how the system manages multiple corrections for a single speech hypothesis. This could impact the accuracy and effectiveness of the personalized ASR model.
What is the impact of the TTL on the storage of corrections and pseudo-corrections?
The abstract mentions the use of a time to live (TTL) for storing corrections and pseudo-corrections, but it does not elaborate on how this affects the overall performance and efficiency of the system.
Original Abstract Submitted
on-device processor(s) of a client device may store, in on-device storage and in association with a time to live (ttl) in the on-device storage, a correction directed to asr processing of audio data. the correction may include a portion of a given speech hypothesis that was modified to an alternate speech hypothesis. further, the on-device processor(s) may cause an on-device asr model to be personalized based on the correction. moreover, and based on additional asr processing of additional audio data, the on-device processor(s) may store, in the on-device storage and in association with an additional ttl in the on-device storage, a pseudo-correction directed to the additional asr processing. accordingly, the on-device processor(s) may cause the on-device asr model to be personalized based on the pseudo-correction to prevent forgetting by the on-device asr model.
