Passive Contextual Data Acquisition for Speech Recognition
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Solution Overview
Problem
Current speech recognition systems lack the ability to effectively utilize contextual information passively gathered before an active speech recognition request, which can enhance the accuracy and relevance of speech analysis.
Innovation Solution
A method and system that extracts contextual information from conversations while the speech recognition system is inactive and applies it to enhance speech analysis by updating models such as the acoustic, language, and contextual models, even when the system is not explicitly activated, using a device with a microphone to listen to conversations and transmit them to a server for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the speech recognition system remains inactive to conserve energy and resources, then energy consumption is reduced, but contextual information from ongoing conversations cannot be captured
Solution Approach 1:
The system performs preliminary speech recognition processing in the background before an explicit user request is made. The processor continuously analyzes audio input and extracts contextual information, preparing data structures and updating models in advance. This allows the system to have contextual information ready when the user actually makes a request, resolving the contradiction by capturing information proactively while maintaining low power consumption during inactive periods.
2Loss of information
If the system continuously processes speech in the background to capture contextual information, then contextual awareness is improved, but computational resources and energy consumption increase
Solution Approach 1:
Instead of performing complete speech recognition processing continuously, the system performs partial processing in the background - extracting only contextual information and key features while deferring full speech analysis until needed. The processor selectively processes audio streams, focusing on identifying speakers, topics, and contextual cues rather than transcribing every word. This partial action approach captures essential contextual information while significantly reducing computational overhead and energy consumption compared to continuous full processing.
3Ease of operation
If speech recognition is activated only when explicitly requested, then system responsiveness to user intent is maintained, but accuracy of speech analysis decreases due to lack of contextual information
Solution Approach 1:
The system prepares contextual information and updates speech recognition models in advance, before the user makes an explicit request. During background operation, the processor continuously analyzes audio input, identifies speakers, extracts topics, and builds contextual representations. When the user activates speech recognition, the system immediately has access to pre-processed contextual information, enabling accurate speech analysis from the outset without requiring lengthy warm-up periods or retraining.
4Loss of information
If the system processes all audio input continuously, then complete contextual information is captured, but device complexity and processing overhead increase
Solution Approach 1:
The speech recognition system is divided into separate functional modules: a background contextual information extraction module that runs continuously with minimal processing, and a foreground speech recognition module that handles explicit user requests. The background module segments processing tasks into lightweight operations such as speaker identification, topic detection, and contextual feature extraction, while the foreground module handles full speech recognition. This segmentation allows the system to maintain complete contextual awareness without requiring all processing components to be active simultaneously, thereby reducing overall device complexity and processing overhead.
Data Source
AI summary
Speech recognition systems often process speech by employing models and analyzing audio data. An embodiment of the method and corresponding system described herein allow for passive monitoring of, for example, conversation between user(s) to determine context to use to prime model(s) for later speech recognition requests submitted to the speech recognition system. The embodiment improves the results of the speech recognition system by updating speech recognition model(s) with contextual information of the conversation. This increases the probability that the speech recognition system interprets the conversation to contextually relevant information.