Endpoint Prediction in Speech Processing Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech processing systems face challenges in accurately determining the endpoint of speech input, leading to errors from premature processing of incomplete inputs or increased latency from delayed processing.
Innovation Solution
A speech processing system that employs an ASR component with endpoint prediction, which transcribes speech and predicts potential endpoints based on acoustic features and transcript analysis, and an endpoint decision component that makes final endpoint decisions using ASR results, endpoint data, and output from downstream components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system waits longer to determine speech endpoint, then accuracy of endpoint detection is improved, but processing latency increases
Solution Approach 1:
The system performs preliminary endpoint prediction using the ASR component during speech transcription, generating potential endpoint candidates before final endpoint decision is made. This allows the system to prepare multiple hypotheses in advance, reducing the time needed for final endpoint determination while maintaining accuracy.
Solution Approach 2:
The endpoint detection process is segmented into multiple stages: initial endpoint prediction by ASR, intermediate endpoint candidate generation, and final endpoint decision. This segmentation allows different components to work at different processing speeds and accuracies, balancing overall system latency and accuracy.
2Productivity
If the system processes speech input faster, then processing latency is reduced, but accuracy of endpoint detection deteriorates
Solution Approach 1:
The ASR component serves as an intermediary that performs preliminary speech-to-text conversion and endpoint prediction. This intermediary processing allows the main endpoint decision component to work with text data rather than raw audio, significantly speeding up processing while maintaining or improving endpoint detection accuracy through multiple analysis passes.
3Device complexity
If the system uses simple endpoint detection, then device complexity is reduced, but endpoint detection accuracy deteriorates
Solution Approach 1:
The system merges endpoint detection functionality with the existing ASR component. The ASR component performs both speech transcription and endpoint prediction simultaneously, eliminating the need for separate endpoint detection hardware or software. This merging reduces overall system complexity while improving accuracy through the combined power of speech recognition and endpoint analysis.
4Measurement precision
If the system uses complex endpoint detection, then endpoint detection accuracy is improved, but device complexity increases
Solution Approach 1:
The ASR component is designed with multi-functionality, serving both speech transcription and endpoint prediction purposes. This universal component eliminates the need for dedicated complex endpoint detection systems, as the ASR component's language models and speech analysis capabilities are leveraged for both functions, reducing overall system complexity while maintaining high accuracy.
Data Source
AI summary
A speech-processing system may determine potential endpoints in a user's speech. Such endpoint prediction may include determining a potential endpoint in a stream of audio data, and may additionally including determining an endpoint score representing a likelihood that the potential endpoint represents an end of speech representing a complete user input. When the potential endpoint has been determined, the system may publish a transcript of speech that preceded the potential endpoint, and send it to downstream components. The system may continue to transcribe audio data and determine additional potential endpoints while the downstream components process the transcript. The downstream components may determine whether the transcript is complete; e.g., represents the entirety of the user input. Final endpoint determinations may be made based on the results of the downstream processing including automatic speech recognition, natural language understanding, etc.