Objective Exploring applications of automatic speech recognition and text-to-speech
technologies in hearing assessment and evaluations of hearing aids. Design Review …

Non-intrusive intelligibility prediction is important for its application in realistic scenarios,
where a clean reference signal is difficult to access. The construction of many non-intrusive …

Improving the user's hearing ability to understand speech in noisy environments is critical to
the development of hearing aid (HA) devices. For this, it is important to derive a metric that …

Speech intelligibility prediction methods are necessary for hearing aid development.
However, many such prediction methods are categorized as intrusive metrics because they …

Neural networks have been successfully used for non-intrusive speech intelligibility
prediction. Recently, the use of feature representations sourced from intermediate layers of …

An accurate objective speech intelligibility prediction algorithms is of great interest for many
applications such as speech enhancement for hearing aids. Most algorithms measures the …

Because a reference signal is often unavailable in real-world scenarios, reference-free
speech quality and intelligibility assessment models are important for many speech …

Self-supervised speech representations (SSSRs) have been successfully applied to a
number of speech-processing tasks, eg as feature extractor for speech quality (SQ) …

Deep neural networks (DNNs) trained by self-supervised learning (SSL) have recently been
shown to produce representations similar to brain activations for the same speech input …

This paper proposes neural models to predict Speech Intelligibility (SI), both by prediction of
established SI metrics and of human speech recognition (HSR) on the 1st Clarity Prediction …