AI Garbled Speech Detection From Encoded Voice Frames
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Solution Overview
Problem
Garbled speech in mobile communication networks, particularly in 2G, 3G, 4G, and 5G networks, is a well-known issue that causes incomprehensible or silent voice calls due to mismatched ciphering parameters, and existing detection methods suffer from high false detection rates and resource-intensive processing.
Innovation Solution
An AI-based approach using machine learning models, such as Random Forest or Neural Networks, to detect garbled speech by analyzing selected information elements of encoded speech frames, reducing the need for decoding and minimizing false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to identify garbled speech, then garbled speech can be detected, but false detection rate is high and processing is resource-intensive
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with an AI-based machine learning system. The ML model analyzes encoded speech frame characteristics to detect garbled speech, substituting conventional correlation-based detection with intelligent pattern recognition that achieves higher accuracy and lower false detection rates without being resource-intensive
Solution Approach 2:
The patent changes the detection parameters by using specific information elements (IEs) from encoded speech frames as input features for the ML model. By selecting and analyzing particular parameters (such as frame number, ciphering parameters, and speech activity indicators) rather than entire speech signals, the system achieves accurate detection with reduced processing requirements
2Measurement precision
If AI-based detection is used to reduce false detections, then detection accuracy improves, but model complexity increases
Solution Approach 1:
The patent extracts only the necessary information elements (IEs) from encoded speech frames to serve as input features for the ML model. By taking out and analyzing only relevant parameters (frame number, ciphering parameters, speech activity indicators) rather than processing entire speech frames, the system maintains high detection accuracy while minimizing model complexity and processing requirements
Solution Approach 2:
The patent applies partial action by using a simplified ML model that processes only selected information elements rather than complete speech frames. This partial processing approach provides sufficient detection accuracy for garbled speech identification while keeping the model complexity and computational resources required at acceptable levels
3Measurement precision
If decoding is performed to detect garbled speech, then detection can be performed, but processing time and resources increase
Solution Approach 1:
The patent performs preliminary detection on encoded speech frames before full decoding occurs. By analyzing information elements from the encoded frame structure (such as frame number, ciphering parameters, and speech activity indicators) while the data is still in encoded form, the system enables early detection of garbled speech, reducing processing time and allowing for earlier correction actions
Data Source
AI summary
An AI-based approach to Garbled speech (GS) detection. Machine learning (ML) models are created that can distinguish between GS speech and non-GS speech with high accuracy. The machine learning models take as input an encoded speech frame that has passed a CRC check. The input data/predictors to the models are a selected set of information elements (IEs) (i.e., a set of one or more bits) of the encoded speech frame. The selected IEs are a part of the input parameters to the speech decoder. It is possible to operate on single encoded speech frames, in contrast to using decoded frames, which requires taking a previous encoded frame into account for being able to perform the decoding.