Dynamic Noise Adaptation Model for ASR Robustness

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Solution Overview

Problem

Existing automatic speech recognition (ASR) systems face challenges in designing noise-robust models that balance computational complexity and performance, particularly in varying noise conditions, where conventional dynamic noise adaptation can sometimes degrade performance even in well-characterized noise environments.

Innovation Solution

The introduction of a Null Noise Model that competes with the current DNA model through Bayesian model selection and re-weighting, allowing for adaptive inference and weighting of noise models to improve speech recognition in low Signal-to-Noise Ratio (SNR) conditions without degrading performance in clean conditions, using a band-quantized Gaussian mixture model to decompose noise into transient and evolving components.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If dynamic noise adaptation (DNA) is applied to improve ASR performance in noisy conditions, then noise robustness is improved, but ASR performance may degrade in clean conditions or when noise conditions are well-characterized by the acoustic models

Engineering Contradiction:

Improvenoise robustnessVSAvoidperformance across varying noise conditions

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system dynamically adapts the noise compensation strength based on detected noise conditions. The DNA model is applied with variable intensity - stronger compensation in high-noise conditions and reduced or no compensation in clean conditions, allowing the system to optimize performance adaptively across different acoustic environments

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes the parameter of noise compensation strength based on detected conditions. By monitoring acoustic characteristics and determining when noise modeling is beneficial, the system adjusts the degree of DNA application, transforming the fixed parameter approach into a variable one that responds to environmental conditions

Inventive Principle:

Principle #35Parameter changes

2Reliability

If explicit noise modeling is applied to improve noise robustness, then performance in noisy conditions improves, but computational complexity increases

Engineering Contradiction:

Improvenoise robustnessVSAvoidcomputational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

Instead of always applying full noise modeling, the system applies partial noise compensation only when and where it is beneficial. The DNA model is selectively applied based on noise condition detection, performing partial action rather than complete action, thereby reducing unnecessary computational complexity while maintaining noise robustness when needed

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS8972256B2System and method for dynamic noise adaptation for robust automatic speech recognition

Publication Date: 2015.03.03 CERENCE OPERATING CO

AI summary

A speech processing method and arrangement are described. A dynamic noise adaptation (DNA) model characterizes a speech input reflecting effects of background noise. A null noise DNA model characterizes the speech input based on reflecting a null noise mismatch condition. A DNA interaction model performs Bayesian model selection and re-weighting of the DNA model and the null noise DNA model to realize a modified DNA model characterizing the speech input for automatic speech recognition and compensating for noise to a varying degree depending on relative probabilities of the DNA model and the null noise DNA model.