5 citations · 10 across the 5 of their papers we have counts for
8 papers
Adapting Diarization-Conditioned Whisper for End-to-End Multi-Talker Speech Recognition
Martin Kocour, Martin Karafiat, Alexander Polok +3
We propose a speaker-attributed (SA) Whisper-based model for multi-talker speech recognition that combines target-speaker modeling with serialized output training (SOT). Our approa…
BUT CHiME-7 system description
Martin Karafiát, Karel Veselý, Igor Szöke +5
This paper describes the joint effort of Brno University of Technology (BUT), AGH University of Krakow and University of Buenos Aires on the development of Automatic Speech Recogni…
Jointly Trained Transformers models for Spoken Language Translation
Hari Krishna Vydana, Martin Karafi'at, Katerina Zmolikova +2
Conventional spoken language translation (SLT) systems are pipeline based systems, where we have an Automatic Speech Recognition (ASR) system to convert the modality of source from…
BUT Opensat 2019 Speech Recognition System
Martin Karafiát, Murali Karthick Baskar, Igor Szöke +3
The paper describes the BUT Automatic Speech Recognition (ASR) systems submitted for OpenSAT evaluations under two domain categories such as low resourced languages and public safe…
Analysis of Multilingual Sequence-to-Sequence speech recognition systems
Martin Karafiát, Murali Karthick Baskar, Shinji Watanabe +3
This paper investigates the applications of various multilingual approaches developed in conventional hidden Markov model (HMM) systems to sequence-to-sequence (seq2seq) automatic…
Promising Accurate Prefix Boosting for sequence-to-sequence ASR
Murali Karthick Baskar, Lukáš Burget, Shinji Watanabe +3
In this paper, we present promising accurate prefix boosting (PAPB), a discriminative training technique for attention based sequence-to-sequence (seq2seq) ASR. PAPB is devised to…
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling
Jaejin Cho, Murali Karthick Baskar, Ruizhi Li +6
Sequence-to-sequence (seq2seq) approach for low-resource ASR is a relatively new direction in speech research. The approach benefits by performing model training without using lexi…
Residual Memory Networks: Feed-forward approach to learn long temporal dependencies
Murali Karthick Baskar, Martin Karafiat, Lukas Burget +3
Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep R…