Hybrid Neuromuscular Speech Input System
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Solution Overview
Problem
Existing speech recognition systems lack the ability to effectively incorporate contextual information from user movements and muscle activations, leading to suboptimal performance in accuracy and speed.
Innovation Solution
The integration of neuromuscular signals, recorded using electromyography (EMG), to enhance speech recognition by providing musculo-skeletal representations and allowing for gesture-based control of speech recognition operations, such as modifying formatting, punctuation, and interaction modes, using a hybrid neuromuscular/speech input system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition systems use only audio input, then the system simplicity is maintained, but the recognition accuracy and speed are suboptimal
Solution Approach 1:
The patent combines audio signals with neuromuscular signals (EMG) from wearable sensors to create a hybrid input system. The speech recognizer processes both types of signals simultaneously, merging acoustic information with musculo-skeletal data to improve recognition accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The speech recognition system is designed to handle multiple input modalities (audio and neuromuscular signals) through a unified processing framework. The same speech recognizer can process pure audio input or hybrid audio-EMG input, making the system multi-functional and adaptable to different input conditions without requiring separate specialized systems.
2Productivity
If speech recognition systems process only audio data, then the processing speed is limited, but adding neuromuscular signals increases processing complexity
Solution Approach 1:
The system performs preliminary processing of neuromuscular signals to extract musculo-skeletal representations (such as hand position, finger configuration, and movement intent) before feeding them into the speech recognizer. This pre-processing step prepares the EMG data in a format that accelerates recognition by providing contextual information about the user's intended input, reducing the computational burden during real-time processing.
3Measurement precision
If the system uses hybrid neuromuscular/speech input, then accuracy and speed improve, but the ease of operation decreases due to multiple input modes
Solution Approach 1:
The system automatically detects and adapts to the user's preferred input mode by analyzing the presence and quality of both audio and neuromuscular signals. It self-adjusts the processing weights and parameters based on the current input conditions, eliminating the need for users to manually configure or switch between modes. The system serves itself by intelligently selecting the optimal processing strategy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves speech recognition accuracy and speed by incorporating contextual information from user movements and muscle activations, enabling more intuitive and efficient interaction with speech recognition systems.
Implementation Method 1
speech data provided as input to the system is augmented with neuromuscular signals (e.g., recorded using electromyography (EMG))
Data Source
AI summary
Systems and methods for text input based on neuromuscular information. The system includes a plurality of neuromuscular sensors, arranged on one or more wearable devices, wherein the plurality of neuromuscular sensors is configured to continuously record a plurality of neuromuscular signals from a user, at least one storage device configured to store one or more trained statistical models, and at least one computer processor programmed to obtain the plurality of neuromuscular signals from the plurality of neuromuscular sensors, provide as input to the one or more trained statistical models, the plurality of neuromuscular signals or signals derived from the plurality of neuromuscular signals, and determine based, at least in part, on an output of the one or more trained statistical models, one or more linguistic tokens.