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20182024
most citedA Comparative Study on Transformer vs RNN in Speech Applications

788 citations · 1.4k across the 24 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

eess.AS2019★ 90 cited

Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition

Nanxin Chen, Shinji Watanabe, Jesús Villalba +1

Recently very deep transformers have outperformed conventional bi-directional long short-term memory networks by a large margin in speech recognition. However, to put it into produ…

eess.AS2019

Feature Enhancement with Deep Feature Losses for Speaker Verification

Saurabh Kataria, Phani Sankar Nidadavolu, Jesús Villalba +3

Speaker Verification still suffers from the challenge of generalization to novel adverse environments. We leverage on the recent advancements made by deep learning based speech enh…

eess.AS2019★ 9 cited

Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings

Erica Cooper, Cheng-I Lai, Yusuke Yasuda +4

While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zer…

cs.CL2019★ 788 cited

A Comparative Study on Transformer vs RNN in Speech Applications

Shigeki Karita, Nanxin Chen, Tomoki Hayashi +10

Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS…

cs.CL2019★ 18 cited

ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks

Cheng-I Lai, Nanxin Chen, Jesús Villalba +1

We present JHU's system submission to the ASVspoof 2019 Challenge: Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT). Anti-spoofing has gathered more and more at…