Speech Transcription Using Visual Context for Ambiguity Resolution
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Solution Overview
Problem
Live speech transcription often results in ambiguous or erroneous transcriptions due to the lack of contextual information, leading to user frustration.
Innovation Solution
An electronic device that transcribes speech by incorporating visual information as an additional input, generating a semantic description of the scene, and modifying transcriptions based on this context to resolve ambiguities, particularly for words with low confidence levels or homonyms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If live speech is transcribed without visual information, then the transcription process is simple and fast, but the transcription accuracy deteriorates due to ambiguous words and lack of context
Solution Approach 1:
The patent combines audio input processing with visual input processing to improve transcription accuracy. The system merges speech recognition with image recognition and natural language processing to resolve ambiguities, such as distinguishing between homonyms like 'flour' and 'flower' based on visual context from cooking scenes.
Solution Approach 2:
The patent introduces a context model as an intermediary component that processes visual information and generates semantic descriptions. This context model acts as a mediator between the audio transcription system and visual input, providing contextual information that resolves ambiguities in speech transcription without requiring direct integration of all processing components.
2Measurement precision
If visual information is incorporated into speech transcription, then transcription accuracy improves through contextual clarity, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of visual inputs to generate context information before final transcription completion. By pre-processing visual data to extract relevant contextual features and semantic descriptions, the system prepares context information in advance that can quickly resolve ambiguities when they arise during speech transcription, reducing overall processing time.
Solution Approach 2:
The system applies contextual information selectively rather than processing all visual data completely. It uses partial processing of visual inputs - only extracting and applying the specific contextual features needed to resolve transcription ambiguities - rather than performing exhaustive analysis of all visual information, thus reducing computational overhead while maintaining accuracy improvements.
3Reliability
If contextual information is added to speech transcription, then ambiguous words are resolved, but the device complexity increases due to additional processing requirements
Solution Approach 1:
The context model serves as an intermediary that handles the complexity of visual processing and semantic analysis, isolating this complexity from the core speech recognition system. This modular approach allows the transcription system to benefit from contextual information while the complexity is managed in a separate, dedicated component.
Solution Approach 2:
The context model is designed to handle multiple types of contextual information and various transcription scenarios through a unified processing framework. It can process different visual inputs, generate semantic descriptions, and resolve various types of ambiguities (homonyms, context-dependent meanings) using the same multi-functional system, reducing overall complexity compared to having separate specialized components for each function.
Data Source
AI summary
A method can include receiving audio input of speech, receiving visual input while receiving the audio input, generating a semantic description based on the visual input, and presenting a transcription of the speech based on the audio input and the semantic description.