Text-to-Speech Conversion Using CRF Reading Sequence
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Solution Overview
Problem
Current audio readers for electronic documents lack the ability to identify relationships between words, intuit the presence of tables, and require user-defined reading sequences, limiting their effectiveness in reading electronic documents such as PDFs.
Innovation Solution
A system and method for text-to-speech conversion that segments characters into segments based on metadata and spatial information, identifies relationships between these segments using a Conditional Random Field (CRF) Sequential Labeling Model, and determines a reading sequence to generate audio accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio readers simply read out text in a fixed sequence without analysis, then the device complexity is low, but the reading accuracy and ability to identify relationships between words deteriorates
Solution Approach 1:
The patent segments the electronic document into multiple segments based on metadata and spatial information, identifying relationships between segments using a Conditional Random Field (CRF) Sequential Labeling Model. This segmentation approach enables the system to understand document structure and reading sequences without requiring overly complex global analysis, thereby improving reading accuracy while controlling device complexity.
Solution Approach 2:
The system performs preliminary analysis by identifying relationships between segments and determining reading sequences before generating audio output. The CRF model pre-processes spatial and metadata information to establish segment relationships in advance, which improves reading accuracy without adding significant runtime complexity to the audio generation process.
2Ease of operation
If audio readers require user input for reading sequence, then the system complexity is low, but the ease of operation deteriorates
Solution Approach 1:
The patent implements self-service by enabling the audio reader to automatically determine reading sequences based on spatial information and metadata analysis. The system uses CRF-based relationship identification to autonomously establish reading orders without requiring user input, significantly improving ease of operation. The automated nature of this process does require increased device complexity to handle the analysis and determination logic.
Data Source
AI summary
The present disclosure relates to methods, non-transitory computer readable media, and devices for text-to-speech conversion of electronic documents. An electronic document comprising one or more pages comprising a plurality of characters and a plurality of first segments is received. The plurality of characters is segmented into a plurality of second segments based on first metadata associated with the plurality of characters. A first relationship between each of the plurality of second segments is identified based on the first metadata associated with the plurality of characters, second metadata associated with the plurality of first segments, and spatial information associated with the plurality of segments. A reading sequence of the electronic document is determined based on the first relationship. An audio is then generated based on the reading sequence of the electronic document.