8 papers with code • 0 benchmarks • 0 datasets
State-of-the-art text-independent speaker verification systems typically use cepstral features or filter bank energies as speech features.
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.
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.
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.
The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals.
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.
The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise.
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.