
Galina LavrentyevaSpeech Technology Center
Galina Lavrentyeva
PhD
About
41
Publications
19,077
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1,130
Citations
Publications
Publications (41)
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 str...
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 train...
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 TD...
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 str...
This paper presents a description of STC Ltd. systems submitted to the NIST 2021 Speaker Recognition Evaluation for both fixed and open training conditions. These systems consists of a number of diverse subsystems based on using deep neural networks as feature extractors. During the NIST 2021 SRE challenge we focused on the training of the state-of...
Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions according to the results obtained for early NIST SRE (Speaker Recognition Evaluation) datasets. From the practical point of view, taking into account the increased interest in virtual assistants (such as Amazon Alexa, Google H...
The widely acknowledged vulnerability of automatic speaker verification systems (ASV) to various spoofing attacks requires the development of countermeasures robust to unforeseen spoofing trials. In this paper we consider deep learning approach based on Light CNN architecture and its modification for replay attack detection on the base of ASVspoof2...
This paper presents the Speech Technology Center (STC) speaker recognition (SR) systems submitted to the VOiCES From a Distance challenge 2019. The challenge's SR task is focused on the problem of speaker recognition in single channel distant/far-field audio under noisy conditions. In this work we investigate different deep neural networks architec...
This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and physical access use-case scenari...
The article describes the new approach for quality improvement of automated dialogue systems for customer support service. Analysis produced in the paper demonstrates the dependency of the quality of the retrieval-based dialogue system quality on the choice of negative responses. The proposed approach implies choosing the negative samples according...
Subject of Research. The present paper is devoted to the attacks detection problem on voice biometric systems (spoofing-attacks) in telephone channel. Nowadays, spoofing detection is under the high interest in the field of voice speaker authentication. The results of the Automatic Speaker Verification Spoofing and Countermeasures Challenge in 2015...
This paper presents an interactive liveness detection approach against presentation attacks. It aims to minimize the impact on the user, who is only asked to produce single head movements. The described approach combines two methods: (1) single-photo liveness estimation based on CNN implementation, and (2) interactive liveness estimation based on h...
Deep learning is still not a very common tool in speaker verification field. We study deep convolutional neural network performance in the text-prompted speaker verification task. The prompted passphrase is segmented into word states - i.e. digits -to test each digit utterance separately. We train a single high-level feature extractor for all state...
In this paper we present Doppelganger mining - a method to learn better face representations. The main idea of this method is to maintain a list with the most similar identities for each identity in the training set. This list is used to generate better mini-batches by sampling pairs of similar-looking identities ("doppelgangers") together. It is e...
Nowadays spoofing detection is one of the priority research areas in the field of automatic speaker verification.The success of Automatic Speaker Verification Spoofing and Countermeasures(ASVspoof) Challenge 2015 confirmed the impressive perspective in detection of unforeseen spoofing trials based on speechsynthesis and voice conversion techniques....
Multi-modal biometric verification systems are in active development and show impressive performance nowadays. However, such systems need additional protection from spoofing attacks. In our paper we present full pipeline of anti-spoofing method (based on our previous work) for bimodal audiovisual verification system. This method allows to evaluate...
Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them. Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still vulnerable to spoofing attacks. Inthis work we overview different acoustic feature spaces and classifiersto determine r...
This paper presents the Speech Technology Center (STC) replay attack detection systems proposed for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017. In this study we focused on comparison of different spoofing detection approaches. These were GMM based methods, high level features extraction with simple classifier and dee...
This paper presents the Speech Technology Center (STC) replay attack detection systems proposed for Automatic Speaker Verification Spoofing and Countermeasures Challenge 2017. In this study we focused on comparison of different spoofing detection approaches. These were GMM based methods, high level features extraction with simple classifier and dee...
Growing interest in automatic speaker verification (ASV) systems has lead to significant quality improvement of spoofing attacks on them. Many research works confirm that despite the low equal error rate (EER) ASV systems are still vulnerable to spoofing attacks. In this work we overview different acoustic feature spaces and classifiers to determin...
In this paper are presented different approaches for speaker position identification that use a microphone array and known voice models. Comparison of speaker positioning is performed by using acoustic maps based on FBF and PHAT. The goal of the experiments is to find best algorithm parameters and their approbation for different types of noises. Th...
In this paper we consider a language identification system based on the state-of-the-art i-vector method. Paper presents a comparative analysis of different methods for the classification in the i-vector space to determine the most efficient for this task. Experimental results show the reliability of the method based on linear discriminant analysis...
In this paper we consider different approaches of artificial neural networks application for speaker recognition task. We investigated the performance of DNN application at different levels of speaker recognition system: i-vector extraction level and model Back-End level. Results of our study perform high efficiency of the proposed neural network b...
In this paper we propose a preprocessing technique which allows to detect clicks, tones, overloads, clipping, etc., as well as to discover the parts of good-quality speech signal. As a result the performance of the speaker recognition system increases significantly. It should be noted that when describing noise detectors we aim only to provide a fu...
This paper explores the robustness of a text-independent voice verification system against different methods of spoofing attacks based on speech synthesis and voice conversion techniques. Our experiments show that the most dangerous are spoofing attacks based on the speech synthesis, but the use of standard TV-JFA approach based spoof-ing detection...
This article is the proceeding of the priority research direction of the voice biometrics systems spoofing problem. We continue exploring speech synthesis spoofing attacks based on creating a text-to-speech voice. In our work we focused on the completely automatic way to create new voices for text-to-speech system and the investigation of the state...
This paper presents the Speech Technology Center (STC) systems submitted to
Automatic Speaker Verification Spoofing and Countermeasures (ASVspoof)
Challenge 2015. In this work we investigate different acoustic feature spaces
to determine reliable and robust countermeasures against spoofing attacks. In
addition to the commonly used front-end MFCC fe...