Practice and recent evidence show that state-of-the-art (SotA) automatic speech recognition
(ASR) systems do not perform equally well for all speaker groups. Many factors can cause …

Quantifying the uncertainty of probabilistic water quality forecasting induced by missing input
data is fundamentally challenging. This study introduced a novel methodology for …

Predicting the concentration of air pollutants is an effective method for preventing pollution
incidents by providing an early warning of harmful substances in the air. Accurate prediction …

The real-time prediction of ship behavior plays an important role in navigation and intelligent
collision avoidance systems. This study developed an online real-time ship behavior …

Accurate air pollutant prediction allows effective environment management to reduce the
impact of pollution and prevent pollution incidents. Existing studies of air pollutant prediction …

The state of charge and state of health estimations are two of the most crucial functions of a
battery management system, which are the quantified evaluation of driving mileage and …

The performance of automatic speech recognition systems degrades with increasing
mismatch between the training and testing scenarios. Differences in speaker accents are a …

At present, there are some problems in domestic stock market, such as difficulty in extracting
effective features and inaccuracy in stock price forecast. This paper proposes a stock price …

Current automatic speech recognition (ASR) systems trained on native speech often perform
poorly when applied to non-native or accented speech. In this work, we propose to compute …

In the presence of a wide variety of dialects, training dialect-specific models for each dialect
is a demanding task. Previous studies have explored training a single model that is robust …