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 …
Overview of speaker modeling and its applications: From the lens of deep speaker representation learning
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 …
By thoroughly and accurately modeling this information, it can be utilized in various …
MFA: TDNN with multi-scale frequency-channel attention for text-independent speaker verification with short utterances
The time delay neural network (TDNN) represents one of the state-of-the-art of neural
solutions to text-independent speaker verification. However, they require a large number of …
solutions to text-independent speaker verification. However, they require a large number of …
Whisper-SV: Adapting Whisper for low-data-resource speaker verification
Trained on 680,000 h of massive speech data, Whisper is a multitasking, multilingual
speech foundation model demonstrating superior performance in automatic speech …
speech foundation model demonstrating superior performance in automatic speech …
Personalized adversarial data augmentation for dysarthric and elderly speech recognition
Despite the rapid progress of automatic speech recognition (ASR) technologies targeting
normal speech, accurate recognition of dysarthric and elderly speech remains a highly …
normal speech, accurate recognition of dysarthric and elderly speech remains a highly …
End-to-end speaker identification research based on multi-scale SincNet and CGAN
G Wei, Y Zhang, H Min, Y Xu - Neural Computing and Applications, 2023 - Springer
Deep learning has improved the performance of speaker identification systems in recent
years, but it has also presented significant challenges. Typically, data-driven modeling …
years, but it has also presented significant challenges. Typically, data-driven modeling …
Voiceextender: Short-Utterance Text-Independent Speaker Verification With Guided Diffusion Model
Y He, Z Kang, J Wang, J Peng… - 2023 IEEE Automatic …, 2023 - ieeexplore.ieee.org
Speaker verification (SV) performance deteriorates as utterances become shorter. To this
end, we propose a new architecture called VoiceExtender which provides a promising …
end, we propose a new architecture called VoiceExtender which provides a promising …
FA-ExU-Net: the simultaneous training of an embedding extractor and enhancement model for a speaker verification system robust to short noisy utterances
Speaker verification (SV) technology has the potential to enhance personalization and
security in various applications, such as voice assistants, forensics, and access control …
security in various applications, such as voice assistants, forensics, and access control …
Deep representation decomposition for rate-invariant speaker verification
F Tong, S Zheng, H Zhou, X Xie, Q Hong… - arXiv preprint arXiv …, 2022 - arxiv.org
While promising performance for speaker verification has been achieved by deep speaker
embeddings, the advantage would reduce in the case of speaking-style variability. Speaking …
embeddings, the advantage would reduce in the case of speaking-style variability. Speaking …
Length-and noise-aware training techniques for short-utterance speaker recognition
Speaker recognition performance has been greatly improved with the emergence of deep
learning. Deep neural networks show the capacity to effectively deal with impacts of noise …
learning. Deep neural networks show the capacity to effectively deal with impacts of noise …