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20202024
most citedMomentum Pseudo-Labeling for Semi-Supervised Speech Recognition

4 citations · 10 across the 9 of their papers we have counts for

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7 papers · 1 filter

eess.AS2024

Textless Streaming Speech-to-Speech Translation using Semantic Speech Tokens

Jinzheng Zhao, Niko Moritz, Egor Lakomkin +7

Cascaded speech-to-speech translation systems often suffer from the error accumulation problem and high latency, which is a result of cascaded modules whose inference delays accumu…

eess.AS2024

M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses

Yufeng Yang, Desh Raj, Ju Lin +8

The growing popularity of multi-channel wearable devices, such as smart glasses, has led to a surge of applications such as targeted speech recognition and enhanced hearing. Howeve…

eess.AS20211 cited

Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy

Yosuke Higuchi, Niko Moritz, Jonathan Le Roux +1

Pseudo-labeling (PL), a semi-supervised learning (SSL) method where a seed model performs self-training using pseudo-labels generated from untranscribed speech, has been shown to e…

eess.AS2021

Dual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech Recognition

Niko Moritz, Takaaki Hori, Jonathan Le Roux

Attention-based end-to-end automatic speech recognition (ASR) systems have recently demonstrated state-of-the-art results for numerous tasks. However, the application of self-atten…

eess.AS20214 cited

Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition

Yosuke Higuchi, Niko Moritz, Jonathan Le Roux +1

Pseudo-labeling (PL) has been shown to be effective in semi-supervised automatic speech recognition (ASR), where a base model is self-trained with pseudo-labels generated from unla…

eess.AS2021

Capturing Multi-Resolution Context by Dilated Self-Attention

Niko Moritz, Takaaki Hori, Jonathan Le Roux

Self-attention has become an important and widely used neural network component that helped to establish new state-of-the-art results for various applications, such as machine tran…