Domain Specific Endpointing for Speech Recognition
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Solution Overview
Problem
Current speech recognition systems require significant computing resources and bandwidth, and raise privacy concerns due to continuous audio transmission, with imprecise endpointing techniques leading to errors, especially when users pause mid-sentence.
Innovation Solution
A distributed speech processing system that uses a local device to detect a wakeword, only transmitting audio after detection, and employs domain-specific endpointing configurations to determine the completion of utterances based on the type of command being spoken, reducing unnecessary processing and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous audio transmission is used for speech recognition, then recognition accuracy is maintained, but bandwidth usage increases and privacy concerns arise
Solution Approach 1:
The system performs preliminary wakeword detection locally on the device before transmitting audio to the server. This preliminary action filters out non-command audio, reducing bandwidth usage while maintaining recognition accuracy for actual commands.
Solution Approach 2:
The patent implements domain-specific endpointing configurations that are tailored to different command types (e.g., weather queries vs. music playback). Each domain has optimized endpointing parameters that match its specific speech patterns, improving recognition accuracy while minimizing unnecessary audio transmission.
2Measurement precision
If domain-specific endpointing configurations are used, then utterance detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the speech processing system into distinct components: local wakeword detection, domain classification, and domain-specific endpointing. Each component handles a specific aspect of the processing pipeline, improving utterance detection accuracy while organizing complexity into manageable modules.
Solution Approach 2:
The system introduces an intermediary domain classification layer that sits between generic speech detection and domain-specific processing. This intermediary automatically routes audio to appropriate endpointing configurations based on the detected domain, reducing the burden on users to manually configure settings while maintaining high detection accuracy.
3Loss of energy
If audio is transmitted only after wakeword detection, then bandwidth usage reduces, but processing delay increases
Solution Approach 1:
The system uses periodic wakeword detection at configured intervals rather than continuous transmission. This periodic approach significantly reduces bandwidth usage while the intervals are optimized to minimize perceived delay, balancing energy efficiency with responsive user experience.
Data Source
AI summary
An automatic speech recognition (ASR) system detects an endpoint of an utterance based on a domain of the utterance. The ASR system processes a first portion of the utterance to determine the domain and then determines an endpoint of the remainder of the utterance depending on the domain.