Yuya Fujita

Attention-based ASR with Lightweight and Dynamic Convolutions

2019 · arXiv (Cornell University)

End-to-end (E2E) automatic speech recognition (ASR) with sequence-to-sequence models has gained attention because of its simple model training compared with conventional hidden Markov model based ASR. Recently, several studies report the state-of-the-art E2E ASR results …

Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models

2021 · arXiv (Cornell University)

Non-autoregressive (NAR) modeling has gained more and more attention in speech processing. With recent state-of-the-art attention-based automatic speech recognition (ASR) structure, NAR can realize promising real-time factor (RTF) improvement with only small degradation of accuracy …

End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation

2022 · arXiv (Cornell University)

This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech Input for Self-supervised learning representation (IRIS). Compared with conventional E2E ASR models, …