Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Glembek, Ondrej"'
Autor:
Silnova, Anna, Stafylakis, Themos, Mosner, Ladislav, Plchot, Oldrich, Rohdin, Johan, Matejka, Pavel, Burget, Lukas, Glembek, Ondrej, Brummer, Niko
In this paper, we analyze the behavior and performance of speaker embeddings and the back-end scoring model under domain and language mismatch. We present our findings regarding ResNet-based speaker embedding architectures and show that reduced tempo
Externí odkaz:
http://arxiv.org/abs/2203.10300
Autor:
Landini, Federico, Glembek, Ondřej, Matějka, Pavel, Rohdin, Johan, Burget, Lukáš, Diez, Mireia, Silnova, Anna
This paper describes the system developed by the BUT team for the fourth track of the VoxCeleb Speaker Recognition Challenge, focusing on diarization on the VoxConverse dataset. The system consists of signal pre-processing, voice activity detection,
Externí odkaz:
http://arxiv.org/abs/2010.11718
Autor:
Kesiraju, Santosh, Sagar, Sangeet, Glembek, Ondřej, Burget, Lukáš, Černocký, Ján, Gangashetty, Suryakanth V
In this paper, we present a Bayesian multilingual document model for learning language-independent document embeddings. The model is an extension of BaySMM [Kesiraju et al 2020] to the multilingual scenario. It learns to represent the document embedd
Externí odkaz:
http://arxiv.org/abs/2007.01359
Autor:
Zeinali, Hossein, Matějka, Pavel, Mošner, Ladislav, Plchot, Oldřich, Silnova, Anna, Novotný, Ondřej, Profant, Ján, Glembek, Ondřej, Burget, Lukáš
This is a description of our effort in VOiCES 2019 Speaker Recognition challenge. All systems in the fixed condition are based on the x-vector paradigm with different features and DNN topologies. The single best system reaches 1.2% EER and a fusion o
Externí odkaz:
http://arxiv.org/abs/1907.06112
In this work, we continue in our research on i-vector extractor for speaker verification (SV) and we optimize its architecture for fast and effective discriminative training. We were motivated by computational and memory requirements caused by the la
Externí odkaz:
http://arxiv.org/abs/1904.04235
In this work, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker verification (SV) system. We start our approach by carefully designing a data augmentati
Externí odkaz:
http://arxiv.org/abs/1811.07629
In this paper, we present an analysis of a DNN-based autoencoder for speech enhancement, dereverberation and denoising. The target application is a robust speaker recognition system. We started with augmenting the Fisher database with artificially no
Externí odkaz:
http://arxiv.org/abs/1811.02938
In this work we revisit discriminative training of the i-vector extractor component in the standard speaker verification (SV) system. The motivation of our research lies in the robustness and stability of this large generative model, which we want to
Externí odkaz:
http://arxiv.org/abs/1810.13183
Autor:
Matějka, Pavel, Plchot, Oldřich, Glembek, Ondřej, Burget, Lukáš, Rohdin, Johan, Zeinali, Hossein, Mošner, Ladislav, Silnova, Anna, Novotný, Ondřej, Diez, Mireia, “Honza” Černocký, Jan
Publikováno v:
In Computer Speech & Language September 2020 63
Autor:
Rohdin, Johan, Silnova, Anna, Diez, Mireia, Plchot, Oldřich, Matějka, Pavel, Burget, Lukáš, Glembek, Ondřej
Publikováno v:
In Computer Speech & Language January 2020 59:22-35