Server ASR Adaptation via Non-ASR Audio Transmission
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Server-side automatic speech recognition (ASR) systems face challenges in achieving low latency responses, especially when there is no prior knowledge of the speaker or noise environment, due to the need to derive characteristics incrementally and adapt to changing conditions, which limits performance and accuracy.
Innovation Solution
A mobile device captures and transmits non-ASR audio data outside the ASR interaction process to a remote server for adaptation to channel-specific characteristics, enabling the server ASR engine to establish and maintain speaker, channel, and environment information, using pre-processed audio samples or background noise models for improved recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If server-side ASR processes speech recognition remotely with greater resources, then recognition accuracy is improved, but response latency increases
Solution Approach 1:
The system performs preliminary actions by capturing and transmitting non-ASR audio data (ambient noise, channel characteristics) before the actual speech recognition task. This allows the server to pre-adapt to the specific acoustic environment and device characteristics, so that when speech recognition is needed, the adaptation is already in place, reducing the time required for accurate recognition without sacrificing accuracy
2Adaptability or versatility
If the ASR system derives speaker and noise characteristics incrementally from speech inputs, then adaptability to changing conditions is improved, but the time required for accurate recognition increases
Solution Approach 1:
The system performs preliminary action by capturing non-ASR audio data (ambient noise, channel characteristics) before the actual speech recognition task. This allows the server to pre-adapt to the specific acoustic environment and device characteristics, so that when speech recognition is needed, the adaptation is already in place, reducing the time required for accurate recognition without sacrificing accuracy
Solution Approach 2:
The system extracts and separates the adaptation process from the speech recognition process. By taking out non-ASR audio data (ambient noise, channel characteristics) and processing it separately before recognition, the system can maintain continuous adaptation without interfering with the real-time speech recognition flow, thus improving both adaptability and response time
3Measurement precision
If the mobile device transmits non-ASR audio data to the server for adaptation, then ASR performance is improved, but network bandwidth consumption increases
Solution Approach 1:
The system extracts and transmits only the essential adaptation data (non-ASR audio characteristics like ambient noise and channel properties) separately from the main speech recognition data. This selective extraction allows the server to perform effective adaptation while minimizing the amount of data transmitted over the network, thus improving ASR performance without proportionally increasing bandwidth consumption
Solution Approach 2:
The system applies local quality by processing and transmitting only the specific portions of audio data that are most relevant for adaptation (non-ASR audio characteristics) rather than transmitting all audio data. This localized approach ensures that the most critical information for improving ASR performance is transmitted while minimizing overall data transmission
Data Source
AI summary
A mobile device is adapted for automatic speech recognition (ASR). A user interface for interaction with a user includes an input microphone for obtaining speech inputs from the user for automatic speech recognition, and an output interface for system output to the user based on ASR results that correspond to the speech input. A local controller obtains a sample of non-ASR audio from the input microphone for ASR-adaptation to channel-specific ASR characteristics, and then provides a representation of the non-ASR audio to a remote ASR server for server-side adaptation to the channel-specific ASR characteristics, and then provides a representation of an unknown ASR speech input from the input microphone to the remote ASR server for determining ASR results corresponding to the unknown ASR speech input, and then provides the system output to the output interface.