Digital Assistant Visual Context Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Digital assistants face challenges in accurately interpreting user requests due to unclear inputs and lack of consideration for visual context, leading to reduced responsiveness and increased power consumption.
Innovation Solution
The method involves receiving an image, generating questions and captions based on objects within the image, and using these to improve speech recognition results by biasing language models and adding relevant vocabulary, thereby determining the relevance of speech recognition results to the visual context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the digital assistant processes all speech recognition results without visual context, then it maintains broad responsiveness to user input, but it experiences reduced accuracy in understanding user requests and increased power consumption
Solution Approach 1:
The system performs preliminary action by generating questions and captions from the image before processing the user's speech input. This pre-processing of visual context creates a framework that guides subsequent speech recognition, improving accuracy while reducing the need to process all possible speech interpretations, thereby lowering power consumption.
Solution Approach 2:
The system applies local quality by focusing computational resources on speech recognition results that are relevant to the visual context rather than processing all possible interpretations equally. By weighting results based on their relevance to the image content, the system improves accuracy for contextually appropriate interpretations while reducing energy expenditure on irrelevant processing.
2Reliability
If the digital assistant ignores visual context, then it maintains simpler processing operations, but it fails to accurately interpret unclear user requests and provides reduced helpful information
Solution Approach 1:
The system generates questions and captions from the image in advance, creating a structured visual context framework. This preliminary processing establishes relevant topics and entities that guide subsequent speech interpretation, improving reliability without requiring complex real-time analysis of all possible contexts.
Solution Approach 2:
The system introduces an intermediary layer consisting of generated questions and captions that mediate between the raw image and the speech input. This intermediary structure simplifies the processing by providing a focused set of relevant concepts that the speech recognition system can reference, rather than directly comparing speech to all possible image interpretations.
3Productivity
If the digital assistant uses visual context to filter speech recognition results, then it reduces false triggers and improves responsiveness, but it requires additional processing steps to generate and compare questions and captions
Solution Approach 1:
The system performs preliminary action by generating questions and captions from the image before the user provides speech input. This pre-computation of visual context enables rapid filtering and comparison when speech is received, improving responsiveness by avoiding the need for complex real-time analysis during the critical speech processing window.
Solution Approach 2:
The system applies partial action by generating only the specific questions and captions that are most relevant to the image content, rather than exhaustively analyzing all possible aspects. This selective approach provides sufficient context for accurate speech recognition without the overhead of complete image analysis, balancing productivity gains with processing complexity.
Data Source
AI summary
Systems and processes for operating a digital assistant are provided. An example method for processing an image include receiving an image, generating, based on the image, a question corresponding to a first object in the image, generating, based on the image, a caption corresponding to a second object of the image, receiving an utterance from a user, and determining a plurality of speech recognition results from the utterance based on the question and the caption.