Voice Auto-Completion Using Multimodal Context and Privacy Segmentation
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Solution Overview
Problem
Existing voice-based assistant systems face challenges in determining when to provide auto-completions, suggesting suitable candidate hypotheses, delivering auto-completions in suitable modalities, and ensuring user privacy in noisy environments.
Innovation Solution
The system uses a personalized language model based on dialog state, context information, and multimodal signals to generate and deliver voice-based auto-completions and auto-responses, leveraging client-side and server-side processes to enhance user interaction and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system provides voice-based auto-completions in noisy environments, then speech recognition accuracy is improved, but user privacy may be compromised
Solution Approach 1:
The system segments the speech processing into client-side and server-side components. The client-side processes voice input locally to generate auto-completions, while the server-side handles more complex tasks. This segmentation allows speech recognition to occur locally without transmitting sensitive voice data to the server, thereby improving accuracy while protecting user privacy.
Solution Approach 2:
The system introduces an intermediary mechanism that processes voice data through multiple stages - first through client-side automatic speech recognition to convert voice to text, then through a language model to generate candidate completions. This intermediary processing pipeline allows the system to improve recognition accuracy through sophisticated modeling while keeping the actual voice data processing localized and controlled.
2Measurement precision
If the system uses personalized language models to generate auto-completions, then interaction accuracy is improved, but system complexity increases
Solution Approach 1:
The personalized language model system is segmented into distinct functional components: an automatic speech recognition module that converts voice to text, a dialog state tracker that maintains conversation context, and a language model that generates candidate completions. This modular segmentation allows each component to be optimized independently while working together to improve overall interaction accuracy without overwhelming system complexity.
Solution Approach 2:
The language model serves multiple functions within the system - it generates auto-completions, provides candidate hypotheses for speech recognition, maintains dialog state, and adapts to user preferences. This multi-functionality allows a single sophisticated component to address multiple aspects of interaction accuracy without requiring separate specialized systems for each function.
3Adaptability or versatility
If the system processes multimodal signals and context information, then auto-completion relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing of context information and dialog state before generating auto-completions. The dialog state tracker continuously maintains an updated representation of the conversation context, and the system pre-processes multimodal signals to extract relevant features. This preliminary action allows the language model to focus on generating completions using already-prepared context, improving relevance while reducing real-time processing time.
4Ease of operation
If the system provides auto-completions without interrupting user activities, then user convenience is improved, but speech recognition errors increase
Solution Approach 1:
The system provides partial auto-completions that suggest candidate words or phrases without requiring the user to complete the entire speech input. It generates multiple candidate hypotheses and presents them for user selection, allowing the user to confirm or correct the recognition. This partial action approach maintains user convenience by not interrupting their activities while improving speech recognition accuracy through candidate verification.
Solution Approach 2:
The system implements feedback mechanisms where auto-completion suggestions are presented to the user, who can then confirm, reject, or correct them. This feedback loop allows the system to learn from user corrections and improve future speech recognition accuracy. The feedback also enables the system to adapt to user preferences and speech patterns, enhancing both convenience and accuracy over time.
Data Source
AI summary
In one embodiment, a method includes receiving a first input by a user from a client system associated with the user, wherein the first input is in a voice modality, analyzing the first input to generate one or more candidate hypotheses, determining one or more modalities for presenting output generated by the one or more computing systems to the user at the client system, and sending instructions to the client system for presenting one or more suggested auto-completions corresponding to one or more of the candidate hypotheses, respectively, wherein each suggested auto-completion comprises the corresponding candidate hypothesis, and wherein the one or more suggested auto-completions are presented in the one or more determined modalities.