This paper focuses on the problem of query by example spoken term detection (QbE-STD) in
zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on …

Voice pathology detection (VPD) aims to accurately identify voice impairments by analyzing
speech signals. This study proposes models based on deep learning (DL) for binary …

We cast the query by example spoken term detection (QbE-STD) problem as subspace
detection where query and background subspaces are modeled as union of low …

We propose an information theoretic framework for quantitative assessment of acoustic
models used in hidden Markov model (HMM) based automatic speech recognition (ASR) …

Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian
mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the …

Towards the goal of improving acoustic modeling for automatic speech recognition (ASR),
this work investigates the modeling of senone subspaces in deep neural network (DNN) …

State of the art query by example spoken term detection (QbE-STD) systems in zero-
resource conditions rely on representation of speech in terms of sequences of class …

Developments of noise robustness techniques are vital to the success of automatic speech
recognition (ASR) systems in face of varying sources of environmental interference. Recent …

Deep neural network (DNN) acoustic models yield posterior probabilities of senone classes.
Recent studies support the existence of low-dimensional subspaces underlying senone …

This paper shows that exemplar-based speech processing using class-conditional posterior
probabilities admits a highly effective search strategy relying on posteriors' intrinsic sparsity …