Cyber-physical systems are starting to adopt neural network (NN) models for a variety of
smart sensing applications. While several efforts seek better NN architectures for system …

In most sensing applications, the measurements generated by sensor networks are noisy
and usually annotated with some measure of uncertainty. The question that we address in …

In several applications of machine listening, predicting how well an automatic speech
recognition system will perform before the actual decoding enables the system to adapt to …

The increasing availability of multiple sensory devices on or near a human body has opened
brand new opportunities to leverage redundant sensory signals for powerful sensing …

In order to improve the ASR performance in noisy environments, distorted speech is typically
pre-processed by a speech enhancement algorithm, which usually results in a speech …

The Sensing tasks, such as evaluating voice clarity, are difficult for computers.(SQA).
Traditional approaches to SQA have favored objective techniques that require the existence …

Wideband codecs such as AMR-WB or EVS are widely used in (mobile) speech
communication. Evaluation of coded speech quality is often performed subjectively by an …

Automatic speech recognition (ASR) in noisy environments remains a challenging goal.
Recently, the idea of estimating the uncertainty about the features obtained after speech …

Neural networks (NNs) have drastically improved the performance of mobile and embedded
applications but lack measures of “reliability” estimation that would enable reasoning over …

Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of
incorporating physics-based domain knowledge into neural network models for many …