Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Speech enhancement is a critical component of many user-oriented audio applications, yet
current systems still suffer from distorted and unnatural outputs. While generative models …

The emergence of the Internet of things and the widespread deployment of diverse
computing systems have led to the formation of heterogeneous multi-agent systems (MAS) …

Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

A field that has directly benefited from the recent advances in deep learning is automatic
speech recognition (ASR). Despite the great achievements of the past decades, however, a …

Diffusion models have recently shown promising results for difficult enhancement tasks such
as the conditional and unconditional restoration of natural images and audio signals. In this …

In this paper, we propose a unified pre-training approach called UniSpeech to learn speech
representations with both labeled and unlabeled data, in which supervised phonetic CTC …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

Process of information extraction (IE) is used to extract useful information from unstructured
or semi-structured data. Big data arise new challenges for IE techniques with the rapid …