Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

Expanding the language coverage of speech technology has the potential to improve
access to information for many more people. However, current speech technology is …

This paper presents XLSR which learns cross-lingual speech representations by pretraining
a single model from the raw waveform of speech in multiple languages. We build on …

Deep learning, as one of the most currently remarkable machine learning techniques, has
achieved great success in many applications such as image analysis, speech recognition …

Deep learning can achieve higher accuracy than traditional machine learning algorithms in
a variety of machine learning tasks. Recently, privacy-preserving deep learning has drawn …

Recent work proposed the concept of backdoor attacks on deep neural networks (DNNs),
where misclassification rules are hidden inside normal models, only to be triggered by very …

TensorFlow is a machine learning system that operates at large scale and in heterogeneous
environments. Tensor-Flow uses dataflow graphs to represent computation, shared state …

TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using TensorFlow …

We present SpeechStew, a speech recognition model that is trained on a combination of
various publicly available speech recognition datasets: AMI, Broadcast News, Common …