Direction-Based Speech Endpointing for ASR Systems
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Solution Overview
Problem
Current speech recognition systems face challenges with bandwidth usage, privacy concerns, and inefficient resource allocation due to continuous audio transmission, and imprecise endpointing in noisy environments with multiple audio sources.
Innovation Solution
A distributed speech processing system that uses a local device to detect a wakeword, filters audio data based on direction and duration to determine the endpoint of speech, and weights hypotheses to distinguish between desired and undesired audio sources, reducing unnecessary processing and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous audio transmission is used for speech recognition, then speech recognition accuracy is improved, but bandwidth usage increases and privacy concerns worsen
Solution Approach 1:
The system performs preliminary audio processing and endpoint detection locally before transmission. By identifying speech endpoints and filtering audio segments locally, the system prepares data in advance to transmit only relevant portions, reducing bandwidth usage while maintaining recognition accuracy.
Solution Approach 2:
The system extracts and transmits only the relevant speech segments rather than continuous audio streams. By taking out specific audio portions that contain actual speech content and excluding silent or irrelevant segments, the system reduces transmission data volume while preserving recognition quality.
2Measurement precision
If continuous audio transmission is used for speech recognition, then speech recognition accuracy is improved, but privacy concerns worsen
Solution Approach 1:
The system extracts and transmits only the necessary speech segments containing actual command information, excluding unnecessary audio portions. This extraction approach minimizes the amount of personal audio data transmitted, thereby reducing privacy risks while maintaining sufficient data for accurate recognition.
Solution Approach 2:
The system performs preliminary filtering and endpoint detection locally before transmission, preparing only the essential speech content for remote processing. This preliminary action ensures that minimal personal audio data is exposed during transmission, addressing privacy concerns while preserving recognition accuracy.
3Measurement precision
If all audio data is processed by remote server, then speech recognition accuracy is improved, but resource allocation efficiency worsens
Solution Approach 1:
The system segments the speech processing task into local and remote components. Local devices perform preliminary processing including audio filtering and endpoint detection, while remote servers handle complex recognition tasks. This segmentation distributes computational resources efficiently, improving overall system productivity while maintaining accuracy.
Solution Approach 2:
The system performs preliminary audio processing and endpoint detection locally before sending data to the remote server. This preliminary action reduces the amount of data requiring remote processing, optimizing resource allocation by ensuring remote servers focus only on essential recognition tasks rather than handling all audio data.
4Device complexity
If traditional endpointing is used in noisy environments with multiple audio sources, then system simplicity is maintained, but endpointing precision worsens
Solution Approach 1:
The system applies direction-based filtering to distinguish audio from different spatial locations. By assigning different directional characteristics to different audio sources, the system can selectively process audio from the desired direction while filtering out noise from other directions, improving endpointing accuracy in noisy environments.
Solution Approach 2:
The system introduces spatial dimension information (audio direction) to the endpointing process. By considering the directional aspect of audio sources in addition to temporal characteristics, the system can more accurately identify speech endpoints even in noisy environments with multiple audio sources, enhancing precision without excessive complexity.
Data Source
AI summary
A system for determining an endpoint of an utterance during automatic speech recognition (ASR) processing that accounts for the direction and duration of the incoming speech. Beamformers of the ASR system may identify a source direction of the audio. The system may track the duration speech has been received from that source direction so that if speech is detected in another direction, the original source speech may be weighted differently for purposes of determining an endpoint of the utterance. Speech from a new direction may be discarded or treated like non-speech for purposes of determining an endpoint of speech from an original direction.