In this paper, we propose a personalized neural speech codec, envisioning that
personalization can reduce the model complexity or improve perceptual speech quality …

This work presents self-supervised learning methods for monaural speaker-specific (ie,
personalized) speech enhancement models. While general-purpose models must broadly …

In realistic speech enhancement settings for end-user devices, we often encounter only a
few speakers and noise types that tend to reoccur in the specific acoustic environment. We …

Speech enhancement is crucial both for human and machine listening applications. Over the
last decade, the use of deep learning for speech enhancement has resulted in tremendous …

Personalized speech enhancement (PSE) models can improve the audio quality of
teleconferencing systems by adapting to the characteristics of a speaker's voice. However …

With the advances in deep learning, speech enhancement systems benefited from large
neural network architectures and achieved state-of-the-art quality. However, speaker …

Although deep learning (DL) has achieved notable progress in speech enhancement (SE),
further research is still required for a DL-based SE system to adapt effectively and efficiently …

This dissertation introduces several machine learning algorithms for developing
personalized speech enhancement (PSE) systems. In particular, we investigate the data …

A personalization framework to adapt compact models to test time environments and
improve their speech enhancement (SE) performance in noisy and reverberant conditions is …

Automatic speech recognition (ASR) training can utilize multiple experts as teacher models,
each trained on a specific domain or accent. Teacher models may be opaque in nature …