activity
20162020
most citedBootstrapping deep music separation from primitive auditory grouping principles

6 citations · 20 across the 5 of their papers we have counts for

collaborators

17 papers

cs.CL20203 cited

Multi-Pass Transformer for Machine Translation

Peng Gao, Chiori Hori, Shijie Geng +2

In contrast with previous approaches where information flows only towards deeper layers of a stack, we consider a multi-pass transformer (MPT) architecture in which earlier layers…

eess.AS2020

AutoClip: Adaptive Gradient Clipping for Source Separation Networks

Prem Seetharaman, Gordon Wichern, Bryan Pardo +1

Clipping the gradient is a known approach to improving gradient descent, but requires hand selection of a clipping threshold hyperparameter. We present AutoClip, a simple method fo…

eess.AS2020

Detecting Audio Attacks on ASR Systems with Dropout Uncertainty

Tejas Jayashankar, Jonathan Le Roux, Pierre Moulin

Various adversarial audio attacks have recently been developed to fool automatic speech recognition (ASR) systems. We here propose a defense against such attacks based on the uncer…

eess.AS20204 cited

Unsupervised Speaker Adaptation using Attention-based Speaker Memory for End-to-End ASR

Leda Sarı, Niko Moritz, Takaaki Hori +1

We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The proposed model contains a me…

eess.AS20205 cited

End-to-End Multi-speaker Speech Recognition with Transformer

Xuankai Chang, Wangyou Zhang, Yanmin Qian +2

Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-ch…

cs.SD2020

Streaming automatic speech recognition with the transformer model

Niko Moritz, Takaaki Hori, Jonathan Le Roux

Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art results in end-to-end automatic speech recognition (ASR). Recently, the transformer architectur…