Ambient Noise-Based ASR Processing Selection
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Solution Overview
Problem
Current virtual assistant systems rely heavily on server-based processing for speech recognition and response, which can be inefficient due to factors like ambient noise levels, leading to suboptimal performance and resource wastage.
Innovation Solution
An electronic device is equipped with a processor that determines whether to perform automated speech recognition (ASR) based on ambient noise information, using AI algorithms to decide between local processing and server-based processing, and can transmit audio signals to the server when noise levels are high, optimizing resource usage and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server-based processing is used for speech recognition, then processing power and accuracy are improved, but response time increases and resource wastage occurs
Solution Approach 1:
The system segments speech recognition processing into two parts: local preprocessing and keyword detection performed by the electronic device, and full ASR processing performed by the server. This segmentation allows the device to handle simple cases locally for fast response while offloading complex cases to the server for high accuracy.
Solution Approach 2:
The system dynamically selects the processing mode (local or server-based) based on real-time conditions such as ambient noise levels, network status, and device state. This dynamic adaptation optimizes the balance between response time and recognition accuracy for each specific situation.
2Productivity
If local ASR processing is performed, then response time is reduced and resource efficiency is improved, but accuracy decreases in high noise environments
Solution Approach 1:
The electronic device performs self-service by conducting local keyword detection and preliminary processing of audio signals. This allows the device to handle simple recognition tasks independently without server intervention, improving response time and resource efficiency for straightforward cases.
Solution Approach 2:
The system dynamically adjusts the processing strategy based on ambient noise levels. In low-noise environments, local processing is sufficient and is used to maximize efficiency. In high-noise environments, the system transitions to server-based processing to maintain accuracy.
3Measurement precision
If device selection is based on ambient noise information, then speech recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses ambient noise level as a key parameter to determine the processing mode. By monitoring this physical parameter and comparing it against thresholds, the system simplifies the decision-making process while maintaining high recognition accuracy across different environmental conditions.
Solution Approach 2:
The system continuously monitors ambient noise levels and uses this feedback to adjust the processing strategy in real-time. This feedback mechanism allows the system to adapt to changing environmental conditions without requiring complex predictive models or multiple decision parameters.
Data Source
AI summary
Providing a response to a user's speech or utterance by obtaining context information of the electronic device or a user of the electronic device, determine whether the electronic device or an external device is to perform automated speech recognition (ASR) of the user's speech or utterance, based on the context information, and provide a response to the user's speech or utterance based on a result of the electronic device or the external device performing the ASR.