Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

We introduce VoxPopuli, a large-scale multilingual corpus providing 100K hours of
unlabelled speech data in 23 languages. It is the largest open data to date for unsupervised …

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus
suitable for speech research. The dataset is derived from read audiobooks from LibriVox …

Convolutional neural networks (CNN) have shown promising results for end-to-end speech
recognition, albeit still behind other state-of-the-art methods in performance. In this paper …

We propose a new end-to-end neural acoustic model for automatic speech recognition. The
model is composed of multiple blocks with residual connections between them. Each block …

We present competitive results using a Transformer encoder-decoder-attention model for
end-to-end speech recognition needing less training time compared to a similarly …

We study pseudo-labeling for the semi-supervised training of ResNet, Time-Depth
Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq …

We present state-of-the-art automatic speech recognition (ASR) systems employing a
standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder …

We revisit self-training in the context of end-to-end speech recognition. We demonstrate that
training with pseudo-labels can substantially improve the accuracy of a baseline model. Key …