Customized Speech Embeddings for Low-Latency On-Device Intent Detection
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Solution Overview
Problem
Existing voice interface devices lack efficient on-device speech processing capabilities due to resource constraints, necessitating reliance on networked systems for ASR and NLU processes, which can be cumbersome and resource-intensive.
Innovation Solution
Implementing customized embeddings that generate reference embeddings from voice commands, allowing devices to perform speech processing locally by analyzing runtime embeddings against stored reference embeddings, reducing the need for off-device processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice interface devices rely on networked systems for ASR and NLU processes, then speech processing capability is improved, but device resource usage increases and network dependency worsens
Solution Approach 1:
The speech processing system is segmented into multiple components: ASR model for transcription, NLU model for intent recognition, and embedding models for representation. These segmented models can be selectively executed based on device capabilities and network availability, allowing the system to distribute computational load efficiently between device and cloud resources.
Solution Approach 2:
Reference embeddings are pre-computed and stored in the embedding store during device setup or initialization. This preliminary action allows the device to perform fast local similarity comparisons without needing to execute full ASR and NLU pipelines during runtime, significantly reducing real-time resource consumption while maintaining speech processing capability.
2Use of energy by moving object
If voice interface devices perform speech processing locally, then resource usage is reduced, but processing accuracy may worsen
Solution Approach 1:
Embeddings serve as an intermediary representation between raw audio input and final intent interpretation. The system converts audio to embeddings, then compares embeddings against reference embeddings to determine intent. This intermediary approach enables accurate local processing by transforming the problem into a similarity comparison task that can be performed efficiently on-device with maintained precision.
Solution Approach 2:
The system creates compressed embedding representations of speech data that capture essential semantic information in a compact form. These embeddings are copied and stored in the embedding store, allowing the device to perform accurate similarity-based intent recognition without needing to store or process full audio recordings, thus maintaining accuracy while reducing resource requirements.
3Measurement precision
If devices require constant network connectivity for ASR and NLU, then processing accuracy is improved, but latency increases
Solution Approach 1:
Reference embeddings are pre-computed and cached in the embedding store during initialization or offline periods. During runtime, the device performs fast local similarity comparisons against these pre-computed embeddings, eliminating the need for real-time network communication and reducing latency to milliseconds while maintaining accuracy through the preserved embedding representations.
Solution Approach 2:
The system implements local processing quality by maintaining an on-device embedding store with reference embeddings tailored to the specific device context and user preferences. This local quality optimization allows the device to accurately process speech locally for common intents while only needing network connectivity for updating embeddings or handling novel intents, thereby reducing latency for everyday operations.
Data Source
AI summary
Systems and methods for speech processing utilizing customized embeddings include receiving a first textual representation of first audio data and intent data indicating an intent of a first voice command. A first embedding may be generated from the textual representation and stored on the device. Second audio data representing a second voice command may be received and a second embedding may be generated therefrom. The first embedding may be determined to have at least a threshold similarity to the second embedding, and an intent may be determined that is associated with the second voice command. An action may be performed utilizing the intent.