Context-Based Endpoint Detection for Speech Signals
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Solution Overview
Problem
Conventional endpointing algorithms for digital assistants are inefficient and prone to errors due to difficulties in distinguishing user speech from background noise, especially in cases of overlapping conversations, and fail to account for individual speech characteristics, leading to premature endpoint detection and user frustration.
Innovation Solution
A context-based endpoint detection method that identifies the endpoint of a spoken request by receiving user input, associating it with context, generating a probability of endpoint location, and determining if it exceeds a threshold, thereby accurately determining the endpoint of speech signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional energy-based endpointing algorithms are used, then the system can operate with simple processing, but the endpoint detection accuracy deteriorates when speech overlaps with background conversation
Solution Approach 1:
The patent transforms the endpoint detection problem from energy-domain analysis to probability-domain analysis. Instead of using fixed energy thresholds, the system computes context-dependent probability scores that dynamically adapt to background conditions. This allows the system to maintain high detection accuracy in complex acoustic environments while managing computational complexity through probabilistic modeling.
Solution Approach 2:
The patent introduces probability scores as an intermediary between raw audio energy features and endpoint detection decisions. These probability scores serve as a mediator that integrates multiple contextual factors (background noise levels, speech characteristics, pause patterns) to make more accurate endpoint determinations than direct energy thresholding alone.
2Reliability
If conventional endpointing algorithms are used, then the processing speed is fast, but the reliability deteriorates when users speak slowly or include long pauses
Solution Approach 1:
The patent performs preliminary computation of context probabilities and speech characteristics before making endpoint detection decisions. By pre-computing contextual probability models and speech patterns, the system can make more reliable endpoint determinations without requiring excessive processing time during actual speech recognition, thus maintaining both reliability and speed.
Solution Approach 2:
The system uses feedback from contextual probability analysis to continuously adjust endpoint detection thresholds. By monitoring speech characteristics and background conditions in real-time, the system adapts its detection criteria to maintain high reliability regardless of speech pace or pause duration, preventing premature endpoint detection.
3Loss of information
If conventional endpointing is used, then the system operates with low computational overhead, but the loss of information increases due to premature endpoint detection
Solution Approach 1:
The patent applies partial probability computation selectively - computing full contextual probabilities only when background noise or speech characteristics suggest potential endpoint ambiguity. In clear, unambiguous situations, the system uses simpler energy-based detection, thus reducing computational energy while preventing information loss in critical cases.
Data Source
AI summary
The present disclosure generally relates to context-based endpoint detection in user speech input. A method for identifying an endpoint of a spoken request by a user may include receiving user input of natural language speech including one or more words; identifying at least one context associated with the user input; generating a probability, based on the at least one context associated with the user input, that a location in the user input is an endpoint; determining whether the probability is greater than a threshold; and in accordance with a determination that the probability is greater than the threshold, identifying the location in the user input as the endpoint.