Nvidia corporation (20240161749). SCENE-AWARE SPEECH RECOGNITION USING VISION-LANGUAGE MODELS simplified abstract
Contents
- 1 SCENE-AWARE SPEECH RECOGNITION USING VISION-LANGUAGE MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SCENE-AWARE SPEECH RECOGNITION USING VISION-LANGUAGE MODELS - 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
SCENE-AWARE SPEECH RECOGNITION USING VISION-LANGUAGE MODELS
Organization Name
Inventor(s)
Gal Chechik of Ramat Hasharon (IL)
SCENE-AWARE SPEECH RECOGNITION USING VISION-LANGUAGE MODELS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161749 titled 'SCENE-AWARE SPEECH RECOGNITION USING VISION-LANGUAGE MODELS
Simplified Explanation
The patent application describes a system that generates a latent space model of a scene or video and applies this latent space, along with candidate sentences formed from digital audio, to a vision-language matching model to improve speech-to-text conversion accuracy.
- Latent space model generation for scenes or videos
- Utilization of latent space and candidate sentences from digital audio in a vision-language matching model
- Enhancement of accuracy in speech-to-text conversion
- Embedding of scene features in latent space
- Embedding of digital audio
- Transcribing/interpreting digital audio embedding using vision-language matching model
Potential Applications
This technology could be applied in various fields such as:
- Video surveillance systems
- Automated transcription services
- Virtual reality and augmented reality applications
- Language translation services
Problems Solved
The technology addresses the following issues:
- Improving accuracy in speech-to-text conversion
- Enhancing the interpretation of digital audio data
- Facilitating communication between different modalities (vision and language)
Benefits
The benefits of this technology include:
- Increased efficiency in transcribing audio data
- Better integration of vision and language processing
- Enhanced user experience in various applications
Potential Commercial Applications
A potential commercial application of this technology could be in:
- Speech recognition software development
- Media content analysis tools
- Language learning platforms
Possible Prior Art
One possible prior art could be the use of latent space models in natural language processing and computer vision tasks. Additionally, existing vision-language matching models may have similarities to the proposed system.
What is the computational complexity of the latent space model generation process?
The computational complexity of the latent space model generation process depends on various factors such as the size of the scene or video data, the dimensionality of the latent space, and the complexity of the feature extraction algorithms used. Generally, generating a latent space model involves significant computational resources and time.
How does the vision-language matching model handle ambiguity in the candidate sentences generated from digital audio?
The vision-language matching model may employ techniques such as context modeling, semantic similarity analysis, and probabilistic reasoning to handle ambiguity in the candidate sentences generated from digital audio. By considering the context of the scene or video and leveraging the latent space embedding, the model can infer the most likely interpretation of the audio data.
Original Abstract Submitted
a system to generate a latent space model of a scene or video and apply this latent space and candidate sentences formed from digital audio to a vision-language matching model to enhance the accuracy of speech-to-text conversion. a latent space embedding of the scene is generated in which similar features are represented in the space closer to one another. an embedding for the digital audio is also generated. the vision-language matching model utilizes the latent space embedding to enhance the accuracy of transcribing/interpreting the embedding of the digital audio.
