Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …

Recent years have seen rapid growth in the deployment of statistical methods for
computational language and speech processing. The current popularity of such methods …

Spoken content retrieval refers to directly indexing and retrieving spoken content based on
the audio rather than text descriptions. This potentially eliminates the requirement of …

This paper is a survey of dictionary screening for the lasso problem. The lasso problem
seeks a sparse linear combination of the columns of a dictionary to best match a given target …

Features derived using sparse representation (SR)-based approaches have been shown to
yield promising results for speech recognition tasks. In most of the approaches, the SR …

Emotion recognition in speech signals is currently a very active research topic and has
attracted much attention within the engineering application area. This paper presents a new …

Solving real-world classification and recognition problems requires a principled way of
modeling the physical phenomena generating the observed data and the uncertainty in it …

Exemplar-based speech enhancement systems work by decomposing the noisy speech as
a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting …

Finding an appropriate feature representation for audio data is central to speech emotion
recognition. Most existing audio features rely on hand-crafted feature encoding techniques …

We propose to model the acoustic space of deep neural network (DNN) class-conditional
posterior probabilities as a union of low-dimensional subspaces. To that end, the training …