Text-Independent Speaker Verification
17 papers with code • 0 benchmarks • 0 datasets
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Deep multi-metric learning for text-independent speaker verification
Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services.
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
Jungjee/RawNet
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Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
Jungjee/RawNet
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In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification.
Utterance-level Aggregation For Speaker Recognition In The Wild
taylorlu/Speaker-Diarization
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The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals.
Multiobjective Optimization Training of PLDA for Speaker Verification
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification
The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise.
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
astorfi/3D-convolutional-speaker-recognition
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In our paper, we propose an adaptive feature learning by utilizing the 3D-CNNs for direct speaker model creation in which, for both development and enrollment phases, an identical number of spoken utterances per speaker is fed to the network for representing the speakers' utterances and creation of the speaker model.
