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 …

In this paper, we provide an overview of the invited and contributed papers presented at the
special session at ICASSP-2013, entitled “New Types of Deep Neural Network Learning for …

Deep neural networks have recently become the gold standard for acoustic modeling in
speech recognition systems. The key computational unit of a deep network is a linear …

While artificial neural networks have been in existence for over half a century, it was not until
year 2010 that they had made a significant impact on speech recognition with a deep form of …

Standard deep neural network-based acoustic models for automatic speech recognition
(ASR) rely on hand-engineered input features, typically log-mel filterbank magnitudes. In this …

In recent years, machine learning models for automated speech scoring systems were
mainly built using data-driven approaches with handcrafted features as one of the main …

Despite the significant progress in speech recognition enabled by deep neural networks,
poor performance persists in some scenarios. In this work, we focus on far-field speech …

Despite the fact that several sites have reported the effectiveness of convolutional neural
networks (CNNs) on some tasks, there is no deep analysis regarding why CNNs perform …

Mel-filter banks are commonly used in speech recognition, as they are motivated from theory
related to speech production and perception. While features derived from mel-filter banks …

There has been little work on examining how deep neural networks may be adapted to
speakers for improved speech recognition accuracy. Past work has examined using a …