Adaptive Silence Detection in Speech Recognition Systems
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Solution Overview
Problem
Current speech recognition systems face challenges in accurately differentiating between natural pauses and the end of speech, particularly in real-time systems, due to limitations in silence detection methods.
Innovation Solution
A computer-implemented method for speech recognition that adjusts silence detection parameters based on lead and terminal periods, threshold amplitude, and environmental factors, using adaptive techniques such as statistical estimation and pulse code modulation, to accurately terminate speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed silence detection parameters are used, then the system is simple to operate, but the accuracy of differentiating between pauses and end of speech deteriorates
Solution Approach 1:
The patent implements dynamic adjustment of silence detection parameters (lead period, terminal period, threshold amplitude) based on real-time analysis of audio signal characteristics. The system continuously adapts these parameters during operation to match the specific speech patterns and environmental conditions, transforming fixed parameters into dynamic, context-aware values that improve detection accuracy without requiring complex manual configuration
Solution Approach 2:
The system performs self-adjustment of silence detection parameters by automatically analyzing the audio signal characteristics and optimizing parameter values without external intervention. The algorithm independently determines optimal lead periods, terminal periods, and threshold amplitudes based on the observed speech patterns, enabling the system to adapt to different users and environments autonomously
2Measurement precision
If adaptive silence detection is implemented, then the accuracy of speech recognition is improved, but the computational resources required increase
Solution Approach 1:
The patent applies partial adaptation by adjusting only the critical silence detection parameters (lead period, terminal period, threshold amplitude) based on essential signal characteristics rather than performing complete signal processing analysis. This selective adaptation achieves sufficient accuracy improvement while limiting computational overhead to only what is necessary for effective silence detection
Solution Approach 2:
The system optimizes computational efficiency by changing specific parameters (lead period, terminal period, threshold amplitude) rather than redesigning the entire speech recognition system. By focusing parameter adjustments on the silence detection module only, the patent achieves accuracy improvement with minimal additional computational burden
3Adaptability or versatility
If silence detection parameters are adjusted frequently, then the adaptability to different speech patterns is improved, but the system stability deteriorates
Solution Approach 1:
The patent implements periodic adjustment of silence detection parameters rather than continuous adjustment. The system evaluates speech patterns over defined periods and adjusts parameters at appropriate intervals, allowing the system to adapt to different speech patterns while maintaining stability during each evaluation period. This periodic approach prevents excessive parameter changes that could destabilize the system
Data Source
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AI summary
A computer implemented method for speech recognition from an audio signal is provided.The method comprises: obtaining initial values for silence detection parameters comprising:a lead period; a threshold amplitude; and a terminal period; starting to detect an amplitude of the audio signal at a first time T1 of the audio signal; optionally, adjusting the threshold amplitude based on the detected amplitude; starting the speech recognition from a second time T2 of the audio signal, wherein the second time T2 is at or after the first time T1; starting silence detection from the audio signal when lead period has elapsed after the second time T2 comprising: responsive to detecting amplitude below the threshold amplitude for a duration of the terminal period, terminating the speech recognition and the silence detection at a third time T3 of the audio signal; and adjusting the silence detection parameters based on the detected amplitude changes of the audio signal between the first time T1 and the third time T3.