Text-Independent Speaker Verification
13 papers with code • 0 benchmarks • 0 datasets
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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.
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.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
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.
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.
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 is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services.
State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features.
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning
First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examples.