Context-Aware Subtitle Generation for ASR Term Accuracy
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems generate subtitles that are inaccurate due to factors like speaker's age, gender, emotion, volume, accent, background noise, and out-of-vocabulary words, leading to subtitles that are difficult to understand and require significant user interaction for clarification.
Innovation Solution
Systems and methods that utilize contextual data, such as metadata and user comments, to identify and replace or supplement terms in subtitles, improving their accuracy and comprehension for the intended audience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ASR systems generate subtitles directly from speech recognition, then the process is automated and fast, but the accuracy and comprehension of subtitles deteriorate due to out-of-vocabulary words and contextual misunderstandings
Solution Approach 1:
The patent introduces contextual data (metadata, user comments, external knowledge sources) as an intermediary between the ASR system and the final subtitles. This intermediary layer enriches the raw ASR output by incorporating additional context about the media content, speaker, and topic, thereby improving accuracy without sacrificing the automated generation process
Solution Approach 2:
The system performs preliminary actions by gathering and processing contextual data before finalizing the subtitles. Metadata about the media content, speaker demographics, and potential out-of-vocabulary terms are analyzed in advance to pre-adjust the ASR output, improving accuracy before the subtitles are presented to the user
2Device complexity
If ASR systems use basic speech recognition without contextual enhancement, then the system complexity remains low, but the subtitles become difficult to understand and require user interaction for clarification
Solution Approach 1:
Contextual data serves as an intermediary that bridges the gap between simple ASR output and user comprehension. By incorporating metadata about the media content, speaker characteristics, and external knowledge, the system enhances subtitle clarity without requiring complex user interactions for clarification
Solution Approach 2:
The system uses user comments and feedback as contextual data to continuously improve subtitle generation. By analyzing what users find confusing or unclear, the system adapts its contextual enhancement strategies to better meet user comprehension needs
3Measurement precision
If ASR systems process more contextual data to improve subtitle accuracy, then subtitle quality improves, but the operational load on the system increases
Solution Approach 1:
The system applies contextual enhancement locally and selectively rather than uniformly to all subtitles. By identifying specific terms likely to be out-of-vocabulary or confusing based on the media content type and speaker characteristics, the system focuses computational resources only where needed, improving accuracy while managing operational load
Data Source
AI summary
Systems and methods are described for generating subtitles. Utterance data is received. First subtitles are generated for the utterance data. A first term is identified in the first subtitles. Contextual data relating to the utterance data is determined. A replacement term for the first term is determined based on the contextual data. Second subtitles are generated for the utterance data. The second subtitles comprise the replacement term.