Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

As the state-of-the-art machine learning methods in many fields rely on larger datasets,
storing datasets and training models on them become significantly more expensive. This …

Objective To perform a comprehensive review of the use of artificial intelligence (AI) and
machine learning (ML) in dentistry, providing the community with a broad insight on the …

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition
corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and …

In this chapter, we review the evidence and explanations for the modern productivity
paradox and propose a resolution. Namely, there is no inherent inconsistency between …

Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we organize the
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

In this paper, we propose the utterance-level permutation invariant training (uPIT) technique.
uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker …

We describe the latest version of Microsoft's conversational speech recognition system for
the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to …