41 citations · 122 across the 9 of their papers we have counts for
8 papers · 1 filter
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition
Ziyi Ni, Minglun Han, Feilong Chen +4
Enhancing automatic speech recognition (ASR) performance by leveraging additional multimodal information has shown promising results in previous studies. However, most of these wor…
Closing the Gap Between Time-Domain Multi-Channel Speech Enhancement on Real and Simulation Conditions
Wangyou Zhang, Jing Shi, Chenda Li +2
The deep learning based time-domain models, e.g. Conv-TasNet, have shown great potential in both single-channel and multi-channel speech enhancement. However, many experiments on t…
The 2020 ESPnet update: new features, broadened applications, performance improvements, and future plans
Shinji Watanabe, Florian Boyer, Xuankai Chang +12
This paper describes the recent development of ESPnet (https://github.com/espnet/espnet), an end-to-end speech processing toolkit. This project was initiated in December 2017 to ma…
Recent Developments on ESPnet Toolkit Boosted by Conformer
Pengcheng Guo, Florian Boyer, Xuankai Chang +12
In this study, we present recent developments on ESPnet: End-to-End Speech Processing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-…
Training Noisy Single-Channel Speech Separation With Noisy Oracle Sources: A Large Gap and A Small Step
Matthew Maciejewski, Jing Shi, Shinji Watanabe +1
As the performance of single-channel speech separation systems has improved, there has been a desire to move to more challenging conditions than the clean, near-field speech that i…
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi, Xuankai Chang, Pengcheng Guo +5
Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus…