Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble

no code implementations • 24 Sep 2021 • Hiroshi Seki, Takashi Nakano, Koshiro Ikeda, Shinji Hirooka, Takaaki Kawasaki, Mitsutomo Yamada, Shumpei Saito, Toshitaka Yamakawa, Shimpei Ogawa

Besides DivideMix, we used a model ensemble technique, SWA, which also focuses on the noisy label problem, to enhance the effect of the models generated by DivideMix.

Vectorization of hypotheses and speech for faster beam search in encoder decoder-based speech recognition

no code implementations • 12 Nov 2018 • Hiroshi Seki, Takaaki Hori, Shinji Watanabe

In this paper, we propose a parallelism technique for beam search, which accelerates the search process by vectorizing multiple hypotheses to eliminate the for-loop program.

speech-recognition Speech Recognition

End-to-End Multi-Lingual Multi-Speaker Speech Recognition

no code implementations • 27 Sep 2018 • Hiroshi Seki, Takaaki Hori, Shinji Watanabe, Jonathan Le Roux, John R. Hershey

Several multi-lingual ASR systems were recently proposed based on a monolithic neural network architecture without language-dependent modules, showing that modeling of multiple languages is well within the capabilities of an end-to-end framework.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

A Purely End-to-end System for Multi-speaker Speech Recognition

no code implementations • ACL 2018 • Hiroshi Seki, Takaaki Hori, Shinji Watanabe, Jonathan Le Roux, John R. Hershey

In this paper, we propose a new sequence-to-sequence framework to directly decode multiple label sequences from a single speech sequence by unifying source separation and speech recognition functions in an end-to-end manner.

speech-recognition Speech Recognition