The phrase "automatic robustness" is correct and usable in written English.
It can be used in contexts related to technology, engineering, or systems design, where it refers to the inherent ability of a system to maintain performance despite changes or disruptions.
Example: "The software's automatic robustness ensures that it continues to function effectively even under unexpected conditions."
Alternatives: "inherent resilience" or "built-in stability".
A more "robust" robustness check would require tedious manual correction and control of this automatic dropping.
In [26], Narayanan and Wang proposed a feature enhancement algorithm for improving noise robustness of automatic speech recognition systems.
The current approach focusing on automatic speech recognition (ASR) robustness to reverberation and noise can be classified as speech signal processing, robust feature extraction, and model adaptation [1 3].
The main advantage offered by this technique of automatic control is its robustness to modeling inaccuracies, system nonlinearities, and time variation of system parameters.
It is well known that frontal video of the speaker's mouth region contains significant speech information that, when combined with the acoustic signal, can improve accuracy and noise robustness of automatic speech recognition (ASR) systems.
The objective of this paper is threefold: (1) to provide an extensive review of signal subspace speech enhancement, (2) to derive an upper bound for the performance of these techniques, and (3) to present a comprehensive study of the potential of subspace filtering to increase the robustness of automatic speech recognisers against stationary additive noise distortions.
Automatic libraries showed good robustness in terms of size distribution and yield prior to sequencing.
The main contribution of our work is that we propose a computational approach for and an implementation of the automatic estimation of the robustness that applies to a broad class of dynamical properties and a large variety of possible perturbations.
We design a novel speech feature post-processing method based on the extracted intrinsic mode functions to achieve noise-robustness for automatic speech recognition.
Feature detection and tracking algorithms have been designed to obtain an automatic setup and strengthen the robustness to light conditions.
