Mutual Information Speech Intelligibility Enhancement
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Solution Overview
Problem
Existing communication technologies fail to effectively enhance speech intelligibility in noisy environments, as they do not adequately account for both production and interpretation noise, leading to reduced communication clarity for users in various settings.
Innovation Solution
A computer-implemented method and system that optimize speech intelligibility by applying a modification algorithm to adjust the mutual information between the intended and interpreted audio signals, considering production and interpretation noise, and dividing the audio signal into frequency bands for targeted gain adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing communication technologies are used in noisy environments, then communication can occur, but speech intelligibility deteriorates due to production and interpretation noise
Solution Approach 1:
The patent changes the parameters of the audio signal by applying frequency-dependent gains to different frequency bands. This transforms the signal in a way that optimizes mutual information between intended and interpreted speech, thereby improving intelligibility in noisy environments without requiring control over the noise sources themselves.
Solution Approach 2:
The patent replaces traditional noise filtering or suppression mechanical approaches with an information-theoretic optimization approach. Instead of directly manipulating the signal to remove noise, it optimizes the mutual information between transmitted and received signals, achieving better intelligibility through mathematical optimization rather than signal filtering.
2Reliability
If signal processing algorithms are applied to enhance speech intelligibility, then communication clarity improves, but device complexity increases
Solution Approach 1:
The patent segments the audio signal into multiple frequency bands and applies different gain adjustments to each band. This segmentation allows the complex problem of speech enhancement to be broken down into simpler, independent sub-problems that can be processed separately and then recombined, reducing overall computational complexity.
Solution Approach 2:
The patent applies gain adjustments only to specific frequency bands that are most critical for speech intelligibility, rather than processing the entire frequency spectrum uniformly. This partial action approach focuses computational resources on the most important aspects of speech enhancement, reducing unnecessary processing complexity.
3Reliability
If frequency-dependent gain adjustments are applied to optimize mutual information, then speech intelligibility in noise improves, but risk of audio signal distortion increases
Solution Approach 1:
The patent uses mutual information as a feedback metric to guide the optimization of frequency-dependent gains. By continuously evaluating the mutual information between intended and interpreted speech, the system can adjust gains to improve intelligibility while avoiding adjustments that would cause distortion, thus maintaining signal fidelity.
Solution Approach 2:
The patent applies dynamic, frequency-dependent gain adjustments rather than static uniform processing. The gains are optimized based on the specific characteristics of the speech signal and noise conditions, allowing the system to adaptively enhance intelligibility while preserving the natural characteristics of the audio signal across different frequency bands.
Data Source
AI summary
Provided are methods and systems for improving the intelligibility of speech in a noisy environment. A communication model is developed that includes noise inherent in the message production and message interpretation processes, and considers that these noises have fixed signal-to-noise ratios. The communication model forms the basis of an algorithm designed to optimize the intelligibility of speech in a noisy environment. The intelligibility optimization algorithm only does something (e.g., manipulates the audio signal) when needed, and thus if no noise is present the algorithm does not alter or otherwise interfere with the audio signals, thereby preventing any speech distortion. The algorithm is also very fast and efficient in comparison to most existing approaches for speech intelligibility enhancement, and therefore the algorithm lends itself to easy implementation in an appropriate device (e.g., cellular phone or smartphone).