Convolutional neural networks also encountered some problems in the development of
image recognition. The most prominent problem is that it is costly and time-consuming to …

Addressing the limitations of pre-defined dictionaries in image processing, this study
introduces Self-Inspired Bases-based Sparse Encoder (SIBS), a novel approach that …

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 …

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 …

The speech production system is time-varying, multidimensional, and nonlinear. Most
techniques for spoken feature extraction (SFE), which are tools for extracting information …

We propose and analyze a solution to the problem of recovering a block sparse signal with
sparse blocks from linear measurements. Such problems naturally emerge inter alia in the …

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 …

This paper addresses the problem of detecting speech utterances from a large audio archive
using a simple spoken query, hence referring to this problem as “Query by Example Spoken …

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

Sparse representation has been shown to be a powerful modeling framework for
classification and detection tasks. In this paper, we propose a new keyword detection …