Local Text-Response Mapping for Low-Latency Voice Interfaces
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Solution Overview
Problem
Voice-based user interfaces in client devices face challenges with latency, resource consumption, and bandwidth usage due to reliance on remote systems for speech processing, especially in offline or low-bandwidth scenarios, leading to suboptimal user interaction.
Innovation Solution
Implementing a local text-response map on the client device to store mappings between previously processed audio data and corresponding responses, allowing immediate rendering of responses without needing remote processing for recognized utterances that match the map entries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio data is transmitted to a remote system for processing, then speech recognition and semantic analysis functionality can be enhanced, but latency increases and response rendering is delayed
Solution Approach 1:
The system pre-processes audio data locally using a speech-to-text model to generate text before transmission to the remote system. This preliminary local processing reduces the time required for remote processing and enables faster response rendering by having the text ready for immediate use upon receipt of the remote response.
Solution Approach 2:
The processing workflow is segmented into distinct local and remote components. Local processing handles audio-to-text conversion and response rendering, while remote processing handles semantic analysis and action determination. This segmentation allows parallel execution of local and remote tasks, reducing overall latency.
2Measurement precision
If audio data is transmitted to a remote system for processing, then complex semantic analysis can be performed, but bandwidth consumption increases
Solution Approach 1:
The system extracts only the essential audio data and generated text for transmission to the remote system, rather than transmitting redundant or unnecessary data. This extraction approach minimizes bandwidth consumption while preserving the information needed for semantic analysis and response generation.
Solution Approach 2:
By pre-converting audio to text locally, the system reduces the amount of data that needs to be transmitted to the remote system. The remote system receives already-converted text rather than raw audio, significantly reducing bandwidth requirements while maintaining semantic analysis capability.
3Measurement precision
If a client-server approach is used for voice processing, then sophisticated speech recognition can be implemented, but the client device must remain online and connected to remote services
Solution Approach 1:
The system performs preliminary speech-to-text conversion locally on the client device before any remote communication is needed. This local preprocessing capability enables the device to operate offline for basic speech recognition tasks, while still maintaining the option to enhance processing by connecting to remote services when available.
Solution Approach 2:
The client device is equipped with local speech-to-text and text-to-speech models that enable it to independently process speech inputs and generate responses without requiring remote services. This self-service capability ensures continuous operation offline while preserving the ability to leverage remote resources when connected.
4Productivity
If remote system processing is used for all voice inputs, then comprehensive response generation is achieved, but power consumption increases due to continuous network communication
Solution Approach 1:
The system performs preliminary processing steps locally (audio-to-text conversion and response rendering) that consume minimal power compared to continuous network communication. By handling these tasks locally and only engaging remote services when necessary, the system maintains comprehensive response generation capability while significantly reducing overall power consumption.
Solution Approach 2:
Instead of continuous communication with the remote system, the client device periodically engages remote services only when local processing is insufficient or when connectivity is available. This periodic engagement pattern reduces power consumption by eliminating continuous network communication while maintaining comprehensive response capabilities.
Data Source
AI summary
Implementations disclosed herein relate to generating and/or utilizing, by a client device, a text-response map that is stored locally on the client device. The text-response map can include a plurality of mappings, where each of the mappings define a corresponding direct relationship between corresponding text and a corresponding response. Each of the mappings is defined in the text-response map based on the corresponding text being previously generated from previous audio data captured by the client device and based on the corresponding response being previously received from a remote system in response to transmitting, to the remote system, at least one of the previous audio data and the corresponding text.