A systematic review of compressive sensing: Concepts, implementations and applications
Compressive Sensing (CS) is a new sensing modality, which compresses the signal being
acquired at the time of sensing. Signals can have sparse or compressible representation …
acquired at the time of sensing. Signals can have sparse or compressible representation …
Compressive sensing: Methods, techniques, and applications
According to the latest research, it is very much clear that in future we require a huge amount
of data as modern advancement in communication and signal processing generates a large …
of data as modern advancement in communication and signal processing generates a large …
Cognitive speech coding: examining the impact of cognitive speech processing on speech compression
Speech coding is a field in which compression paradigms have not changed in the last 30
years. Speech signals are most commonly encoded with compression methods that have …
years. Speech signals are most commonly encoded with compression methods that have …
Composition of deep and spiking neural networks for very low bit rate speech coding
Most current very low bit rate (VLBR) speech coding systems use hidden Markov model
(HMM) based speech recognition and synthesis techniques. This allows transmission of …
(HMM) based speech recognition and synthesis techniques. This allows transmission of …
Perceptual information loss due to impaired speech production
Phonological classes define articulatory-free and articulatory-bound phone attributes. Deep
neural network is used to estimate the probability of phonological classes from the speech …
neural network is used to estimate the probability of phonological classes from the speech …
Sparse subspace modeling for query by example spoken term detection
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 …
zero-resource scenario. Current state-of-the-art approaches to tackle this problem rely on …
Interpretable phonological features for clinical applications
Instrumental analysis of speech sometimes complements subjective evaluations in speech
and language therapy; however, apart from elemental speech features such as pitch and …
and language therapy; however, apart from elemental speech features such as pitch and …
On structured sparsity of phonological posteriors for linguistic parsing
The speech signal conveys information on different time scales from short (20–40 ms) time
scale or segmental, associated to phonological and phonetic information to long (150–250 …
scale or segmental, associated to phonological and phonetic information to long (150–250 …
Low-rank representation of nearest neighbor phone posterior probabilities to enhance DNN acoustic modeling
G Luyet, P Dighe, A Asaei, H Bourlard - 2016 - infoscience.epfl.ch
We hypothesize that optimal deep neural networks (DNN) class-conditional posterior
probabilities live in a union of low-dimensional subspaces. In real test conditions, DNN …
probabilities live in a union of low-dimensional subspaces. In real test conditions, DNN …
[PDF][PDF] Phonological Posterior Hashing for Query by Example Spoken Term Detection.
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 …
resource conditions rely on representation of speech in terms of sequences of class …