Sergey Novoselov

Sergey Novoselov, Galina Lavrentyeva, Anastasia Avdeeva et al.

Recent advances in unsupervised speech representation learning discover new approaches and provide new state-of-the-art for diverse types of speech processing tasks. This paper presents an investigation of using wav2vec 2.0 deep speech representations for the speaker recognition task. The proposed fine-tuning procedure of wav2vec 2.0 with simple TDNN and statistic pooling back-end using additive angular margin loss allows to obtain deep speaker embedding extractor that is well-generalized across different domains. It is concluded that Contrastive Predictive Coding pretraining scheme efficiently utilizes the power of unlabeled data, and thus opens the door to powerful transformer-based speaker recognition systems. The experimental results obtained in this study demonstrate that fine-tuning can be done on relatively small sets and a clean version of data. Using data augmentation during fine-tuning provides additional performance gains in speaker verification. In this study speaker recognition systems were analyzed on a wide range of well-known verification protocols: VoxCeleb1 cleaned test set, NIST SRE 18 development set, NIST SRE 2016 and NIST SRE 2019 evaluation set, VOiCES evaluation set, NIST 2021 SRE, and CTS challenges sets.

Sergey Novoselov, Vladimir Volokhov, Galina Lavrentyeva

Creating universal speaker encoders which are robust for different acoustic and speech duration conditions is a big challenge today. According to our observations systems trained on short speech segments are optimal for short phrase speaker verification and systems trained on long segments are superior for long segments verification. A system trained simultaneously on pooled short and long speech segments does not give optimal verification results and usually degrades both for short and long segments. This paper addresses the problem of creating universal speaker encoders for different speech segments duration. We describe our simple recipe for training universal speaker encoder for any type of selected neural network architecture. According to our evaluation results of wav2vec-TDNN based systems obtained for NIST SRE and VoxCeleb1 benchmarks the proposed universal encoder provides speaker verification improvements in case of different enrollment and test speech segment duration. The key feature of the proposed encoder is that it has the same inference time as the selected neural network architecture.

Galina Lavrentyeva, Sergey Novoselov, Andrey Shulipa et al.

Deep speaker embedding extractors have already become new state-of-the-art systems in the speaker verification field. However, the problem of verification score calibration for such systems often remains out of focus. An irrelevant score calibration leads to serious issues, especially in the case of unknown acoustic conditions, even if we use a strong speaker verification system in terms of threshold-free metrics. This paper presents an investigation over several methods of score calibration: a classical approach based on the logistic regression model; the recently presented magnitude estimation network MagnetO that uses activations from the pooling layer of the trained deep speaker extractor and generalization of such approach based on separate scale and offset prediction neural networks. An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system. The obtained results demonstrate that there are no serious problems if in-domain development data are used for calibration tuning. Otherwise, a trade-off between good calibration performance and threshold-free system quality arises. In most cases using adaptive s-norm helps to stabilize score distributions and to improve system performance. Meanwhile, some experiments demonstrate that novel approaches have their limits in score stabilization on several datasets.