New waves of consumer-centric applications, such as voice search and voice interaction
with mobile devices and home entertainment systems, increasingly require automatic …

This paper presents the main paradigms for speaker identification, and recent work on
missing data methods to increase robustness. The feature extraction, speaker modeling and …

We propose a feature enhancement algorithm to improve robust automatic speech
recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in the Mel …

The Application of Hidden Markov Models in Speech Recognition Page 1 The Application of
Hidden Markov Models in Speech Recognition Full text available at: http://dx.doi.org/10.1561/2000000004 …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

Speech recognition technology has left the research laboratory and is increasingly coming
into practical use, enabling a wide spectrum of innovative and exciting voice-driven …

This paper proposes to use exemplar-based sparse representations for noise robust
automatic speech recognition. First, we describe how speech can be modeled as a linear …

Today's digital contents are inherently multimedia: text, audio, image, video, and so on.
Video, in particular, has become a new way of communication between Internet users with …

Recently, supervised classification has been shown to work well for the task of speech
separation. We perform an in-depth evaluation of such techniques as a front-end for noise …

An effective way to increase the noise robustness of automatic speech recognition is to label
noisy speech features as either reliable or unreliable (missing), and to replace (impute) the …