Intermediate Speech Data for Private Low-Latency Command Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech processing systems face challenges in efficiently handling complex natural language commands while ensuring privacy by minimizing audio data transmission to remote systems.
Innovation Solution
A system architecture that divides speech processing operations between a local device and a remote system using intermediate data streams, allowing local processing of audio data and sending only processed data to the remote system, enhancing privacy and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio data is transmitted to remote systems for speech processing, then processing accuracy and capabilities are improved, but privacy is compromised and transmission time increases
Solution Approach 1:
The speech processing task is divided into two segments: local processing segment (audio data processing, feature extraction) and remote processing segment (high-level speech analysis, intent recognition). Only processed features and intermediate results are transmitted to the remote system, not the raw audio data, thus maintaining privacy while benefiting from remote processing capabilities.
Solution Approach 2:
An intermediary data processing layer is introduced between the local device and remote system. This intermediary processes audio data locally into intermediate representations (phoneme-level features, word-level contexts) that serve as the transmission medium, preventing direct exposure of raw audio data to remote systems while still enabling accurate speech processing.
2Adaptability or versatility
If audio data is transmitted to remote systems for speech processing, then processing capabilities are improved, but transmission time and latency increase
Solution Approach 1:
Processing is segmented such that time-sensitive audio data processing (transcription, phoneme recognition) is performed locally on the device, while less time-critical high-level processing (intent analysis, context understanding) is performed remotely. This division minimizes transmission time while maintaining comprehensive processing capabilities.
Solution Approach 2:
The local device performs preliminary processing of audio data into intermediate representations before transmission to the remote system. This preliminary action includes extracting phoneme-level features, generating word-level transcriptions, and preparing context information, which reduces the processing burden and transmission time requirements for the remote system.
3Measurement precision
If more audio data is transmitted to remote systems, then speech recognition accuracy is improved, but processing complexity and data volume increase
Solution Approach 1:
The system extracts only the essential features and intermediate results from the audio data processing pipeline (phoneme-level representations, word-level transcriptions, confidence scores) and transmits these to the remote system. This extraction approach maintains the information needed for accurate speech recognition while significantly reducing the volume and complexity of transmitted data compared to sending raw audio streams.
Solution Approach 2:
The data representation is transformed from raw audio waveforms to processed feature parameters (phoneme probabilities, word transcriptions, acoustic features). This parameter transformation reduces data dimensionality and complexity while preserving the information necessary for accurate speech recognition, making the data more efficient for remote processing.
Data Source
AI summary
Some speech processing systems may handle some commands on-device rather than sending the audio data to a second device or system for processing. The first device may have limited speech processing capabilities sufficient for handling common language and/or commands, while the second device (e.g., an edge device and/or a remote system) may call on additional language models, entity libraries, skill components, etc. to perform additional tasks. An intermediate data generator may facilitate dividing speech processing operations between devices by generating a stream of data that includes a first-pass ASR output (e.g., a word or sub-word lattice) and other characteristics of the audio data such as whisper detection, speaker identification, media signatures, etc. The second device can perform the additional processing using the data stream; e.g., without using the audio data. Thus, privacy may be enhanced by processing the audio data locally without sending it to other devices/systems.