Dynamic Endpoint Detection for Speech Recognition Latency
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Solution Overview
Problem
Premature endpoint detection in speech recognition systems leads to increased latency and user frustration, as users often pause during input, causing ASR results to be incomplete, necessitating reinput or additional information, which detracts from the user experience.
Innovation Solution
Implementing dynamic endpoint detection and local caching of NLU results, along with dynamically-generated hints, to reduce premature endpointing and improve user interaction with speech-enabled applications by adjusting timeout values based on user behavior and storing frequently used utterances locally for reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a threshold time for endpointing is used in ASR systems, then the system can detect end of speech, but premature endpoint detection occurs when users pause during input, leading to incomplete ASR results and increased latency
Solution Approach 1:
The system dynamically adjusts the endpointing timeout value based on whether the ASR result enables a valid action. If the initial ASR result is insufficient, the timeout is extended to capture additional audio input, allowing the system to adapt to user pauses without requiring fixed timeout values
Solution Approach 2:
The system uses feedback from the ASR result validation process to determine whether to extend audio processing. By checking if the ASR result enables a valid action, the system can identify premature endpointing and trigger additional audio capture, creating a closed-loop control mechanism
2Measurement precision
If the ASR system waits for complete speech input, then accurate recognition is achieved, but latency increases and user experience deteriorates
Solution Approach 1:
The system performs ASR processing on partial audio input rather than waiting for complete speech. By generating ASR results from available audio segments and validating them, the system can proceed with partial information rather than requiring excessive waiting time for complete input
Solution Approach 2:
The system performs preliminary ASR processing and validation as soon as audio is available, rather than waiting for complete speech. This preliminary action allows the system to identify insufficient results early and trigger additional audio capture only when necessary
3Reliability
If the system requires complete and accurate ASR results before performing actions, then action accuracy is maintained, but additional reinput is required when users pause, increasing complexity
Solution Approach 1:
The system dynamically determines whether to request reinput based on validation results. Rather than always requiring complete input or always accepting partial input, the system adapts its behavior based on whether the current ASR result enables a valid action, simplifying the operational flow
4Loss of information
If the system processes all audio input until timeout, then complete information is captured, but processing time and energy consumption increase
Solution Approach 1:
The system processes audio partially by stopping processing early when a valid ASR result is obtained. Rather than processing all audio until timeout, the system can terminate processing once sufficient information is captured, reducing energy consumption while maintaining information completeness
Data Source
AI summary
The method comprises receive first audio comprising speech from a user of a computing device, detecting an end of speech in the first audio, generating an ASR result based, at least in part, on a portion of the first audio prior to the detected end of speech, determining whether a valid action can be performed by a speech-enabled application installed on the computing device using the ASR result, and processing second audio when it is determined that a valid action cannot be performed by the speech-enabled application using the ASR result.