6 citations · 19 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Language model integration based on memory control for sequence to sequence speech recognition
Jaejin Cho, Shinji Watanabe, Takaaki Hori +4
In this paper, we explore several new schemes to train a seq2seq model to integrate a pre-trained LM. Our proposed fusion methods focus on the memory cell state and the hidden stat…
Transfer learning of language-independent end-to-end ASR with language model fusion
Hirofumi Inaguma, Jaejin Cho, Murali Karthick Baskar +2
This work explores better adaptation methods to low-resource languages using an external language model (LM) under the framework of transfer learning. We first build a language-ind…
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…