Context-Aware Utterance End-Point Detection for Multi-Turn ASR
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems struggle to accurately detect the end-point of a user's utterance, particularly in multi-turn conversations, leading to incomplete sentence interpretation and suboptimal user experience.
Innovation Solution
The system employs context-based detection of utterance end-points by using semantic networks and adjusting timeout thresholds based on the context of the audio signal, ensuring complete sentence recognition through ASR and natural language understanding (NLU) modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the ASR system uses a fixed timeout threshold to detect the end-point of utterance, then the system operation is simple, but the detection accuracy deteriorates when users pause to decide what to say next
Solution Approach 1:
The system dynamically adjusts the timeout threshold based on semantic network completion status. When the current utterance does not complete a semantic network, the system extends the timeout threshold to allow users to continue speaking. This dynamic adjustment resolves the contradiction by making the detection accurate without requiring a completely complex system architecture.
Solution Approach 2:
The system changes the timeout parameter based on semantic context. Instead of using a fixed timeout value, the system modifies the timeout threshold according to whether the semantic network is complete or incomplete. This parameter change approach improves detection accuracy while maintaining relatively simple system operation.
2Reliability
If the ASR system transmits incomplete sentences to processing blocks, then the processing speed is fast, but the interpretation accuracy deteriorates
Solution Approach 1:
The system performs preliminary checking of semantic network completion before transmitting the utterance for processing. By checking whether the semantic network is complete in advance, the system ensures that only complete or potentially complete sentences are transmitted, improving interpretation accuracy without significant delay.
Solution Approach 2:
The system uses feedback from semantic network analysis to determine whether to extend the listening window or transmit the current utterance. This feedback mechanism ensures that incomplete sentences are not prematurely transmitted, improving reliability while managing processing time efficiently.
3Reliability
If the system extends the timeout threshold for all utterances, then the completeness of sentence recognition is improved, but the loss of time increases
Solution Approach 1:
The system applies timeout extension selectively rather than universally. Only utterances that do not complete a semantic network receive extended timeout thresholds. This local quality approach ensures sentence completeness where needed while avoiding unnecessary time loss for complete utterances.
Solution Approach 2:
The system applies timeout extension as a partial action only when semantically necessary. Instead of extending timeout for all utterances (excessive action), the system extends it only for incomplete semantic networks (partial action), balancing completeness with time efficiency.
Data Source
AI summary
Detecting an end-point of user's voice command or utterance with high accuracy is critical in automatic speech recognition (ASR)-based human machine interface. If an ASR system incorrectly detects an end-point of utterance and transmits this incomplete sentence to other processing blocks for further processing, it is likely the processed result would lead to incorrect interpretation. A method includes selecting a first semantic network based on context of the audio signal and more accurately detecting the end-point of user's utterance included in the audio signal based on the first semantic network and also based on at least one timeout threshold associated with the first semantic network.