Context-Guided Decoder Correction for Voice Data Errors
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Solution Overview
Problem
Traditional decoding methods struggle with accuracy and efficiency due to their inability to utilize context information, leading to errors in data transmission and storage, especially in unreliable communication channels or storage devices.
Innovation Solution
The use of context information from higher layers, such as textual, voice, and object recognition data, to create decoding feedback that corrects errors and informs decoding decisions, thereby improving decoder performance by reducing errors and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional decoding methods are used without context information, then the decoder operates with simpler processing, but decoding accuracy deteriorates due to inability to correct errors effectively
Solution Approach 1:
The patent implements feedback by using decoded data from higher layers (such as corrected text from language models or verified voice data) to inform and correct the decoding process at the channel decoding stage. This feedback loop allows the decoder to adjust its decisions based on contextual information, improving accuracy without requiring a complete redesign of the decoder architecture.
Solution Approach 2:
The patent applies preliminary action by performing error detection and context-based validation on decoded data before final output. Higher layers pre-process the decoded information to identify likely errors (such as improbable text sequences or voice anomalies) and provide correction hints back to the decoder, enabling proactive error correction rather than reactive processing.
2Reliability
If context-based feedback is implemented to improve decoding accuracy, then error correction performance improves, but processing time increases due to additional feedback processing
Solution Approach 1:
The patent applies partial action by implementing context-based feedback selectively rather than for all decoded data. The system determines when feedback processing is necessary based on error indicators or confidence thresholds, applying full feedback processing only when needed. This reduces average processing time while maintaining high error correction performance for problematic cases.
3Productivity
If traditional decoding without feedback is used, then processing is faster and simpler, but error detection and correction capabilities are insufficient
Solution Approach 1:
The patent applies preliminary action by performing error detection and context-based validation on decoded data before final output. Higher layers pre-process the decoded information to identify likely errors (such as improbable text sequences or voice anomalies) and provide correction hints back to the decoder, enabling proactive error correction rather than reactive processing.
Data Source
AI summary
Disclosed in some examples are methods, systems, and machine-readable mediums for utilizing context information to create decoding feedback information to improve decoder accuracy and/or performance. In some examples, the context information is from layers of a network stack above the layers in which the decoders are present. The context information may be or be based upon information about previously received and decoded data and/or information about the sender to provide decoding feedback information to the decoder that is used either to correct a previous decoding error or to inform the decoder on which of a plurality of decoding choices is more likely to be correct. This may increase decoding performance by decreasing errors and in some examples, reducing the complexity of choices by eliminating certain decoding possibilities and thus increasing decoder efficiency.