46 citations · 140 across the 12 of their papers we have counts for
6 papers · 1 filter
Sequence-level self-learning with multiple hypotheses
Kenichi Kumatani, Dimitrios Dimitriadis, Yashesh Gaur +4
In this work, we develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR). For untranscribed speech…
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Tae Jin Park, Kenichi Kumatani, Dimitrios Dimitriadis
Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time. Models need to be trained on client…
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes
Rimita Lahiri, Kenichi Kumatani, Eric Sun +1
Multilingual end-to-end(E2E) models have shown a great potential in the expansion of the language coverage in the realm of automatic speech recognition(ASR). In this paper, we aim…
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset
Chengyi Wang, Yu Wu, Shujie Liu +4
Recently, there has been a vast interest in self-supervised learning (SSL) where the model is pre-trained on large scale unlabeled data and then fine-tuned on a small labeled datas…
Dynamic Gradient Aggregation for Federated Domain Adaptation
Dimitrios Dimitriadis, Kenichi Kumatani, Robert Gmyr +2
In this paper, a new learning algorithm for Federated Learning (FL) is introduced. The proposed scheme is based on a weighted gradient aggregation using two-step optimization to of…
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Chengyi Wang, Yu Wu, Yao Qian +5
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC le…