On-Device Utterance Pattern Caching for IoT Voice Latency
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Solution Overview
Problem
Existing systems for controlling internet-of-things devices struggle with efficient processing of voice commands, often requiring robust distributed systems for accurate speech recognition and natural language understanding, which can lead to latency and privacy concerns, especially in environments with poor network connections.
Innovation Solution
The implementation of on-device command caching utilizing utterance patterns, where audio data from user utterances is processed to identify consistent utterance-to-command patterns, allowing voice interface devices to store and utilize these patterns for quick command selection without relying on external systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robust distributed systems are used for speech recognition and natural language understanding, then accuracy is improved, but latency increases and privacy concerns arise
Solution Approach 1:
The system performs preliminary action by identifying and caching utterance-to-command patterns before they are needed. Historical voice commands and their corresponding device commands are collected, processed to identify consistent patterns, and stored in a cache. When a user speaks a command, the system first checks the cache for matching patterns and executes the corresponding device command immediately without waiting for cloud-based speech recognition and natural language understanding processing.
2Measurement precision
If robust distributed systems are used for speech recognition and natural language understanding, then accuracy is improved, but privacy concerns increase
Solution Approach 1:
The system extracts and caches only the essential utterance-to-command mapping patterns from historical data, separating the critical functional information from the full voice command processing pipeline. This extraction allows the system to store and reuse command patterns locally without needing to continuously transmit full audio data to external systems, thereby reducing privacy risks while maintaining command execution accuracy.
Solution Approach 2:
The system implements self-service by autonomously identifying patterns from its own historical command data and caching them for future use. The pattern identification process analyzes the device's own voice command history, extracts consistent utterance-to-command mappings, and stores them locally. This self-directed pattern caching reduces dependence on external cloud processing and minimizes the need to transmit sensitive voice data outside the device.
3Loss of time
If on-device pattern caching is implemented, then latency is reduced and privacy is improved, but system complexity increases
Solution Approach 1:
The system applies partial action by implementing a simplified pattern identification approach that focuses only on the essential functionality needed for caching. Rather than implementing a complete speech recognition and natural language understanding system on-device, the patent uses a targeted pattern identification process that analyzes historical command data to extract consistent utterance-to-command mappings. This partial implementation achieves the latency reduction benefit while avoiding the full complexity of complete on-device speech processing.
Data Source
AI summary
Systems and methods for caching on-device commands utilizing utterance patterns are disclosed. For example, user utterances and resulting selected commands are analyzed to determine which utterances result in consistently-selected commands. Pattern data is generated indicating an association between these utterances and commands. The pattern data is stored in an on-device storage and utilized to select a command to be performed when the associated utterances are identified from audio data. When new features are developed for cloud-side services, a determination is made as to whether commands and utterances associated with the new features are candidates for utterance-to-command patterns, and corresponding pattern data may be sent to the on-device storages.