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Cosine Scoring With Uncertainty for Neural Speaker Embedding
IEEE Signal Processing LettersKong Aik Lee, Qiongqiong Wang
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Disentangled Speaker and Nuisance Attribute Embedding for Robust Speaker Verification [PDF]
IEEE Access, 2020 Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are known to
Woo Hyun Kang, , Min Hyun Han
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Supervised Speaker Embedding De-Mixing in Two-Speaker Environment [PDF]
2021 IEEE Spoken Language Technology Workshop (SLT), 2021 Published at ...
Thomas Hain
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Applied Sciences (Switzerland)Automatic speech recognition (ASR) aims at understanding naturally spoken human speech to be used as text inputs to machines. In multi-speaker environments, where multiple speakers are talking simultaneously with a large amount of overlap, a significant ...
Gil-Jin Jang
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Utterance-Style-Dependent Speaker Verification Using Emotional Embedding with Pretrained Models [PDF]
SensorsBiometric authentication using human physiological and behavioral characteristics has been widely adopted, with speaker verification attracting attention due to its convenience and noncontact nature.
Long Pham Hoang +4 more
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Speaker embedding loss for end-to-end speaker diarization without external embedding networks
EURASIP Journal on Audio, Speech, and Music ProcessingThis paper introduces a novel speaker embedding loss function designed to improve the performance of end-to-end neural diarization (EEND) systems by enhancing speaker discrimination.
Jaehee Jung, Wooil Kim
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Applied Sciences (Switzerland), 2019 Recently, the increasing demand for voice-based authentication systems has encouraged researchers to investigate methods for verifying users with short randomized pass-phrases with constrained vocabulary.
Woo Hyun Kang
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TDNN achitecture with efficient channel attention and improved residual blocks for accurate speaker recognition [PDF]
Scientific ReportsIn recent years, with the advancement of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in speaker recognition, making CNN-based speaker embedding learning the predominant method for speaker verification.
Wenzao Li +6 more
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Adapting Speaker Embeddings for Speaker Diarisation [PDF]
Interspeech 2021, 2021 The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation. The quality of speaker embeddings is paramount to the performance of speaker diarisation systems. Despite this, prior works in the field have directly used embeddings designed only to be effective on the speaker verification task.
Youngki Kwon +5 more
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