Deep speaker embeddings for Speaker Verification: Review and experimental comparison

M Jakubec, R Jarina, E Lieskovska, P Kasak - Engineering Applications of …, 2024 - Elsevier
The construction of speaker-specific acoustic models for automatic speaker recognition is
almost exclusively based on deep neural network-based speaker embeddings. This work …

Overview of speaker modeling and its applications: From the lens of deep speaker representation learning

S Wang, Z Chen, KA Lee, Y Qian… - IEEE/ACM Transactions …, 2024 - ieeexplore.ieee.org
Speaker individuality information is among the most critical elements within speech signals.
By thoroughly and accurately modeling this information, it can be utilized in various …

A survey on deep reinforcement learning for audio-based applications

S Latif, H Cuayáhuitl, F Pervez, F Shamshad… - Artificial Intelligence …, 2023 - Springer
Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence
(AI) by endowing autonomous systems with high levels of understanding of the real world …

Mint: Boosting generalization in mathematical reasoning via multi-view fine-tuning

Z Liang, D Yu, X Pan, W Yao, Q Zeng, X Zhang… - ar** a lightweight speaker embedding extractor (SEE) is crucial for the practical
implementation of automatic speaker verification (ASV) systems. To this end, we recently …

Large-scale learning of generalised representations for speaker recognition

J Jung, HS Heo, BJ Lee, J Lee, H Shim, Y Kwon… - arxiv preprint arxiv …, 2022 - arxiv.org
The objective of this work is to develop a speaker recognition model to be used in diverse
scenarios. We hypothesise that two components should be adequately configured to build …