Journal Article10.1016/J.CSL.2017.08.004
Sparse coding based features for speech units classification
17
TL;DR: Both raw speech samples and mel frequency cepstral coefficients are used as an initial representation for feature extraction and a transformation function known as weighted decomposition (WD) of principal components is used to emphasize the discriminative information present in the PCA-based dictionary.
read more
About: This article is published in Computer Speech & Language. The article was published on 01 Jan 2018. The article focuses on the topics: K-SVD & TIMIT.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Greedy dictionary learning for kernel sparse representation based classifier
TL;DR: Compared to the existing state-of-the-art methods, the proposed method has much less computational complexity, but performs similar for various pattern classification tasks.
21
Greedy double sparse dictionary learning for sparse representation of speech signals
TL;DR: A greedy double sparse (DS) dictionary learning algorithm for speech signals, where the dictionary is the product of a predefined base dictionary, and a sparse matrix, and it is shown that the dictionary can be learned efficiently in the coefficient domain rather than the signal domain.
14
Two-Level Band Selection Framework for Hyperspectral Image Classification
TL;DR: Experimental results indicate that the proposed two-level, PCA-based band selection algorithm can select bands with varying levels of discriminative capabilities to effectively classify hyperspectral images consisting of classes spectrally very similar in nature.
9
Reducing footprint of unit selection based text-to-speech system using compressed sensing and sparse representation
TL;DR: Experimental studies on two different Indian languages suggest that CS/SR based footprint reduction methods can be used as an alternative to existing compression methods employed in USS system.
9
Speech emotion recognition using kernel sparse representation based classifier
Pulkit Sharma,Vinayak Abrol,Abhijeet Sachdev,A. D. Dileep +3 more
- 01 Aug 2016
TL;DR: Experimental results demonstrate that, given a suitable kernel, KSRC with group sparsity constraint performs better as compared to the state-of-the-art support vector machines (SVM) based classifiers.
8
References
•Journal Article
Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.
10K
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Joel A. Tropp,Anna C. Gilbert +1 more
TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case
Joel A. Tropp,Anna C. Gilbert +1 more
- 01 Aug 2007
TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
S. Davis,Paul Mermelstein +1 more
TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
5.3K