Deep Residual Networks have recently been shown to significantly improve the
performance of neural networks trained on ImageNet, with results beating all previous …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

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 …

Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats.
Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) …

Deep learning-based machine learning models have shown significant results in speech
recognition and numerous vision-related tasks. The performance of the present speech-to …

We describe the latest version of Microsoft's conversational speech recognition system for
the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to …

Conversational speech recognition has served as a flagship speech recognition task since
the release of the Switchboard corpus in the 1990s. In this paper, we measure the human …

Arrhythmia is an abnormal heartbeat rhythm, and its prevalence increases with age. An
electrocardiogram (ECG) is a standard tool for detecting cardiac activity. However, because …

Sequence-to-sequence models have shown success in end-to-end speech recognition.
However these models have only used shallow acoustic encoder networks. In our work, we …

Detecting anomalies in time series data is becoming mainstream in a wide variety of
industrial applications in which sensors monitor expensive machinery. The complexity of this …