Rule-Based Speech End-Pointer for Vehicle ASR Noise Isolation
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Solution Overview
Problem
Automatic Speech Recognition (ASR) systems in vehicle environments face challenges in accurately identifying spoken utterances due to background noise and transient noises, leading to false positives and delayed responses to actual commands.
Innovation Solution
A rule-based and dynamic end-pointer system that analyzes audio streams for speech characteristics, such as voiced and non-voiced sounds, and environmental conditions to determine the beginning and end of spoken utterances, using rules based on duration and transitions to improve the accuracy of speech segment identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If energy detection is used to identify speech segments, then the system can detect audio events, but it misinterprets transient noises as spoken utterances causing false positives
Solution Approach 1:
The system changes the detection parameters from simple energy threshold to voice activity detection using spectral characteristics. By analyzing the spectral content and comparing it against learned voice patterns, the system can distinguish between speech and transient noise, reducing false positives while maintaining detection accuracy.
Solution Approach 2:
The system introduces an intermediary voice activity detection module between the energy detection and ASR processing stages. This intermediary layer filters out transient noises by analyzing spectral characteristics before the audio is sent to ASR, preventing false positives without delaying actual speech commands.
2Measurement precision
If energy detection with predetermined time periods is used, then the system can determine speech boundaries, but it causes delayed response to actual commands
Solution Approach 1:
The system implements feedback by continuously monitoring audio characteristics and adjusting the speech boundary detection in real-time. When speech is detected, the system immediately marks the boundary without waiting for predetermined time periods, and the feedback loop continues to refine the detection based on ongoing audio analysis, reducing response delay while maintaining precision.
Solution Approach 2:
The system transitions from static predetermined time periods to dynamic speech boundary detection. The boundaries are determined by real-time analysis of voice activity and spectral characteristics, allowing the system to adapt to varying speech patterns and reduce unnecessary delays while maintaining accurate boundary detection.
3Ease of manufacture
If simple energy threshold detection is used, then the system is easy to implement, but it cannot distinguish between speech and transient noise
Solution Approach 1:
The system evolves from simple energy threshold detection to more sophisticated voice activity detection by changing the analysis parameters to include spectral characteristics. This allows the system to maintain ease of implementation while significantly improving its ability to distinguish between speech and transient noise through multi-parameter analysis.
Data Source
AI summary
A rule-based end-pointer isolates spoken utterances contained within an audio stream from background noise and non-speech transients. The rule-based end-pointer includes a plurality of rules to determine the beginning and/or end of a spoken utterance based on various speech characteristics. The rules may analyze an audio stream or a portion of an audio stream based upon an event, a combination of events, the duration of an event, or a duration relative to an event. The rules may be manually or dynamically customized depending upon factors that may include characteristics of the audio stream itself, an expected response contained within the audio stream, or environmental conditions.