Dynamic Endpoint Detection for Speech Recognition Latency
Find Innovative SolutionsGenerate Solutions
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 is time-consuming and detracts from the user experience.
Innovation Solution
Implementing dynamic endpoint detection and local caching of NLU results, along with dynamically-generated hints on the user interface, to reduce premature endpointing and enhance 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
1Productivity
If a fixed threshold time for endpoint detection is used, then the system can process speech input efficiently, but users who pause during input experience premature endpoint detection leading to incomplete ASR results
Solution Approach 1:
The patent implements dynamic adjustment of the endpoint detection threshold based on characteristics of the speech input itself. The system analyzes features such as speech rate, pause patterns, and utterance structure to adaptively determine when to terminate speech processing, rather than using a fixed threshold. This allows the system to accommodate users who pause during input while maintaining efficient processing for continuous speech.
2Reliability
If the endpoint detection threshold is increased to accommodate user pauses, then fewer premature endpoint detections occur, but processing latency increases for all users
Solution Approach 1:
The patent applies different endpoint detection threshold values to different portions or types of speech input based on their characteristics. Rather than using a single global threshold, the system dynamically determines appropriate threshold levels for each utterance based on local speech features such as detected pause patterns, speech rate variations, and contextual information. This localized adaptation resolves the contradiction by applying higher thresholds only where needed while maintaining lower thresholds for continuous speech.
3Reliability
If the system waits for complete speech input before processing, then ASR results are complete and accurate, but user interaction latency increases
Solution Approach 1:
The patent performs preliminary processing of speech input segments as they become available, rather than waiting for the complete utterance. The system begins ASR processing on initial speech segments while continuing to monitor for additional input, allowing preliminary results to be generated and presented to users before the speech is fully complete. This preliminary action reduces interaction latency while maintaining accuracy through subsequent refinement as more input arrives.
4Measurement precision
If the system processes all speech input completely before responding, then response accuracy is high, but user frustration increases due to perceived unresponsiveness
Solution Approach 1:
The patent implements continuous speech processing where the system begins analyzing and responding to speech input as it arrives, rather than waiting for complete input. Multiple processing stages operate concurrently: initial ASR on received segments, continuous monitoring for additional input, and incremental refinement of results. This continuous action provides users with perceived responsiveness while maintaining high accuracy through ongoing processing, resolving the contradiction between response speed and accuracy.
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.