Audio Device Voice Control Without User-Specific Pre-Training
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Solution Overview
Problem
Conventional audio device interfaces, such as on-product buttons, mobile applications, and voice assistants, are inefficient and frustrating for users, particularly in controlling headphones and speakers.
Innovation Solution
Implementing a machine learning (ML) model that processes user voice inputs to determine control actions for audio devices without requiring pre-training, allowing for efficient and intuitive voice control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional voice assistant control is used, then users can control audio devices verbally, but the control process becomes inefficient and frustrating due to complex interfaces and pre-training requirements
Solution Approach 1:
The patent extracts and removes the pre-training requirement from the voice control system. The ML model is designed to process user input directly without needing pre-training with user-specific data, eliminating a complex setup phase and simplifying the overall system architecture while maintaining voice control functionality.
Solution Approach 2:
The patent segments the ML model processing into distinct modules: audio capture, ML model processing, and control action execution. This segmentation allows each component to be optimized independently and simplifies the overall system structure, making the voice control more efficient and easier to implement without requiring full pre-training.
2Measurement precision
If pre-trained ML models are used for voice control, then accuracy can be improved, but the time and resources required for pre-training increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-configuring the ML model with general audio processing capabilities and control logic before deployment. The model is prepared in advance with the necessary structure and parameters to accurately interpret user input and determine control actions, eliminating the need for time-consuming on-device pre-training while maintaining high accuracy.
Solution Approach 2:
The patent changes the operational parameters of the ML model to function effectively without pre-training. By adjusting the model's architecture and processing parameters, it achieves accurate control action determination directly from user input, sacrificing the time-intensive pre-training phase while preserving measurement precision through optimized parameter configuration.
3Adaptability or versatility
If simple button control is used, then the interface remains simple, but controlling audio devices becomes limiting and less intuitive
Solution Approach 1:
The patent substitutes mechanical button control with a voice-based control system using an ML model. This replacement provides significantly greater control flexibility and adaptability, allowing users to issue complex commands naturally through speech while maintaining ease of operation. The ML model processes diverse voice inputs and translates them into appropriate control actions for various audio device functions.
Data Source
AI summary
Various implementations include approaches for voice control in audio devices. In some cases, a method includes: listening, using at least one audio capture device, for user input to control at least one attribute of an audio device; routing the user input through a machine learning (ML) model to determine a control action for the at least one attribute based on the user input; and causing the determined control action to be performed, wherein the ML model need not have been pre-trained with the user input to determine the control action for the at least one attribute of the audio device.