40 citations · 82 across the 23 of their papers we have counts for
7 papers · 1 filter
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding
Yifan Peng, Siddhant Arora, Yosuke Higuchi +6
Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming. Recent studies achieved promising results by using pre-trained models in…
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss
Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi +1
This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision. M…
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder
Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi +1
We present BERT-CTC-Transducer (BECTRA), a novel end-to-end automatic speech recognition (E2E-ASR) model formulated by the transducer with a BERT-enhanced encoder. Integrating a la…
CTC Alignments Improve Autoregressive Translation
Brian Yan, Siddharth Dalmia, Yosuke Higuchi +4
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment. However fo…
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
Yosuke Higuchi, Brian Yan, Siddhant Arora +3
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC). Our formulation relaxes the con…
ESPnet-ONNX: Bridging a Gap Between Research and Production
Masao Someki, Yosuke Higuchi, Tomoki Hayashi +1
In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks. In contrast, application developers are interested in maki…