Vision-Language Speech Recognition for Scene-Aware Transcription
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Solution Overview
Problem
Existing speech-to-text systems struggle to accurately transform free-form natural language spoken inputs into commands or environmental actions due to the lack of effective utilization of visual context clues, particularly in virtual or augmented reality environments where video datasets fail to provide sufficient context on less-commonly encountered visual objects.
Innovation Solution
The system employs vision-language matching models to generate scores for the match between text inputs and digital images, enhancing the accuracy of speech-to-text conversion by applying a 'reward' based on visual context, using algorithms like Contrastive Language-Image Pre-training (CLIP) to transform features into a latent space and measure similarity between text and image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech-to-text systems are used without visual context, then the system complexity remains low, but the accuracy of transforming free-form natural language into commands deteriorates
Solution Approach 1:
The patent merges speech-to-text conversion with visual context analysis by integrating a vision-language matching model (CLIP) with the automatic speech recognition system. The visual encoder processes video frames to generate image embeddings, which are then matched with text embeddings to provide context-aware scoring that improves transcription accuracy without requiring a complete system redesign
Solution Approach 2:
The patent introduces an intermediary vision-language matching model that acts as a bridge between audio inputs and text outputs. This model computes similarity scores between visual and textual representations, providing contextual guidance to the speech-to-text system without directly modifying the core ASR architecture, thus improving accuracy while maintaining relative system simplicity
2Loss of information
If video datasets are used for training, then audio context is provided, but context on less-commonly encountered visual objects is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training the vision-language matching model on large-scale datasets like CLIP, which includes diverse image-text pairs. This pre-training equips the model with general visual understanding capabilities before it is fine-tuned on specific video datasets, ensuring reliable visual context even for less-common objects
Solution Approach 2:
The patent employs a universal vision-language matching model that can handle diverse visual content across different domains and object types. The CLIP model's multi-functionality allows it to provide reliable visual context for both common and rare objects by leveraging its training on broad, diverse datasets rather than domain-specific data
Data Source
AI summary
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.