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 …

Deep learning is becoming a mainstream technology for speech recognition at industrial
scale. In this paper, we provide an overview of the work by Microsoft speech researchers …

This monograph provides an overview of general deep learning methodology and its
applications to a variety of signal and information processing tasks. The application areas …

Voice-activated intelligent assistants, such as Siri, Google Now, and Cortana, are prevalent
on mobile devices. However, it is challenging to evaluate them due to the varied and …

Measuring the performance of automatic speech recognition (ASR) systems requires
manually transcribed data in order to compute the word error rate (WER), which is often time …

In this paper, several works are proposed to address practi-cal challenges for deploying
RNN Transducer (RNN-T) based speech recognition systems. These challenges are …

In this paper, we present techniques to compute confidence score on the predictions made
by an end-to-end speech recognition model. Our proposed neural confidence measure …

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug
development failure or withdrawal from the market in most cases, showing an emerging …

In this paper we present a confidence estimation system using recurrent neural networks
(RNN) and compare it to a traditional multilayered perception (MLP) based system. The …

In the last years, deep bidirectional recurrent neural networks (DBRNN) and DBRNN with
long short-term memory cells (DBLSTM) have outperformed the most accurate classifiers for …