Search Results for author: Lukáš Burget

Found 16 papers, 7 papers with code

Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation

1 code implementation24 Nov 2020 Katerina Zmolikova, Marc Delcroix, Lukáš Burget, Tomohiro Nakatani, Jan "Honza" Černocký

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation.

Audio and Speech Processing

Text Augmentation for Language Models in High Error Recognition Scenario

1 code implementation11 Nov 2020 Karel Beneš, Lukáš Burget

We examine the effect of data augmentation for training of language models for speech recognition.

Speech Recognition Text Augmentation

Bayesian multilingual topic model for zero-shot cross-lingual topic identification

no code implementations2 Jul 2020 Santosh Kesiraju, Sangeet Sagar, Ondřej Glembek, Lukáš Burget, Suryakanth V. Gangashetty

This paper presents a Bayesian multilingual topic model for learning language-independent document embeddings.

Multiwavelength classification of X-ray selected galaxy cluster candidates using convolutional neural networks

no code implementations10 Jun 2020 Matej Kosiba, Maggie Lieu, Bruno Altieri, Nicolas Clerc, Lorenzo Faccioli, Sarah Kendrew, Ivan Valtchanov, Tatyana Sadibekova, Marguerite Pierre, Filip Hroch, Norbert Werner, Lukáš Burget, Christian Garrel, Elias Koulouridis, Evelina Gaynullina, Mona Molham, Miriam E. Ramos-Ceja, Alina Khalikova

The results of using CNNs on combined X-ray and optical data for galaxy cluster candidate classification are encouraging and there is a lot of potential for future usage and improvements.

Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics

Probabilistic embeddings for speaker diarization

1 code implementation6 Apr 2020 Anna Silnova, Niko Brümmer, Johan Rohdin, Themos Stafylakis, Lukáš Burget

We apply the proposed probabilistic embeddings as input to an agglomerative hierarchical clustering (AHC) algorithm to do diarization in the DIHARD'19 evaluation set.

Speaker Diarization

A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database

no code implementations8 Dec 2019 Hossein Zeinali, Lukáš Burget, Jan "Honza'' Černocký

We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR.

Speech Recognition Text-Dependent Speaker Verification +1

Learning document embeddings along with their uncertainties

2 code implementations20 Aug 2019 Santosh Kesiraju, Oldřich Plchot, Lukáš Burget, Suryakanth V. Gangashetty

We present Bayesian subspace multinomial model (Bayesian SMM), a generative log-linear model that learns to represent documents in the form of Gaussian distributions, thereby encoding the uncertainty in its co-variance.

Topic Models Variational Inference

Detecting Spoofing Attacks Using VGG and SincNet: BUT-Omilia Submission to ASVspoof 2019 Challenge

no code implementations13 Jul 2019 Hossein Zeinali, Themos Stafylakis, Georgia Athanasopoulou, Johan Rohdin, Ioannis Gkinis, Lukáš Burget, Jan "Honza'' Černocký

In this paper, we present the system description of the joint efforts of Brno University of Technology (BUT) and Omilia -- Conversational Intelligence for the ASVSpoof2019 Spoofing and Countermeasures Challenge.

Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery

1 code implementation8 Apr 2019 Lucas Ondel, Hari Krishna Vydana, Lukáš Burget, Jan Černocký

This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages.

Acoustic Unit Discovery

BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers

1 code implementation SEMEVAL 2019 Martin Fajcik, Lukáš Burget, Pavel Smrz

This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019).

General Classification Rumour Detection +1

Promising Accurate Prefix Boosting for sequence-to-sequence ASR

no code implementations7 Nov 2018 Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe, Martin Karafiát, Takaaki Hori, Jan Honza Černocký

In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR.

BUT QUESST 2014 System Description

no code implementations16 Oct 2014 Igor Szöke, Miroslav Skácel, Lukáš Burget

The primary system we submitted was composed of 11 subsystems as the required run.

 Ranked #1 on Keyword Spotting on QUESST (MinCnxe metric)

Dynamic Time Warping Keyword Spotting

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