A review of deep learning techniques for speech processing
The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …
learning. The use of multiple processing layers has enabled the creation of models capable …
Speaker recognition based on deep learning: An overview
Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …
learning has dramatically revolutionized speaker recognition. However, there is lack of …
A review of speaker diarization: Recent advances with deep learning
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 …
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
Lipopolysaccharide-induced model of neuroinflammation: mechanisms of action, research application and future directions for its use
A Skrzypczak-Wiercioch, K Sałat - Molecules, 2022 - mdpi.com
Despite advances in antimicrobial and anti-inflammatory therapies, inflammation and its
consequences still remain a significant problem in medicine. Acute inflammatory responses …
consequences still remain a significant problem in medicine. Acute inflammatory responses …
Multimodal intelligence: Representation learning, information fusion, and applications
Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …
natural language processing since 2010. Each of these tasks involves a single modality in …
Voicefilter: Targeted voice separation by speaker-conditioned spectrogram masking
In this paper, we present a novel system that separates the voice of a target speaker from
multi-speaker signals, by making use of a reference signal from the target speaker. We …
multi-speaker signals, by making use of a reference signal from the target speaker. We …
Speaker recognition for multi-speaker conversations using x-vectors
Recently, deep neural networks that map utterances to fixed-dimensional embeddings have
emerged as the state-of-the-art in speaker recognition. Our prior work introduced x-vectors …
emerged as the state-of-the-art in speaker recognition. Our prior work introduced x-vectors …
End-to-end neural speaker diarization with self-attention
Speaker diarization has been mainly developed based on the clustering of speaker
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
End-to-end neural speaker diarization with permutation-free objectives
In this paper, we propose a novel end-to-end neural-network-based speaker diarization
method. Unlike most existing methods, our proposed method does not have separate …
method. Unlike most existing methods, our proposed method does not have separate …
The third DIHARD diarization challenge
DIHARD III was the third in a series of speaker diarization challenges intended to improve
the robustness of diarization systems to variability in recording equipment, noise conditions …
the robustness of diarization systems to variability in recording equipment, noise conditions …