Improved speaker verification using block sparse coding over joint speaker-channel learned dictionary

Sreeram, Ganji; Haris, B. C.; Sinha, Rohit

doi:10.1109/tencon.2015.7373183

Cited by 4 publications

(2 citation statements)

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“…In the field of sparse representation, the focus is growing due to the application of speech de-noising, source separation, speech encryption, and speech classification based on the sparse model analysis shown in figure 3. DNN is a popular former method that achieves better recognition performance in speaker recognition [13,14,22]. A large number of hidden layers which must be linear or nonlinear are included in this DNN These hidden layers represent the data in the encoded form [11].…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

Singh

Manusha

Balaramakrishna

et al. 2022

IJEER

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The identity of the speakers depends on the phonological properties acquired from the speech. The Mel-Frequency Cepstral Coefficients (MFCC) are better researched for derived the acoustic characteristic. This speaker model is based on a small representation and the characteristics of the acoustic features. These are derived from the speaker model and the cartographic representation by the MFCCs. The MFCC is used for independent monitoring of speaker text. There is a problem with the recognition of speakers by small representation, so proposed the Gaussian Mixture Model (GMM), mean super vector core for training. Unknown vector modules are cleared using rarity and experiments based on the TMIT database. The I-vector algorithm is proposed for the effective improvement of ASR (Automatic Speaker Recognition). The Atom Aligned Sparse Representation (AASR) is used to describe the speaker-based model. The Short Representation Classification (SRC) is used to describe the speaker recognition report. A robust short coding is based on the Maximum Likelihood Estimation (MIE) to clarify the problem in small representation. Strong speaker verification based on a small representation of GMM super vectors. Strong speaker verification based on a small representation of GMM super vectors.

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Section: ░ 1 Introductionmentioning

confidence: 99%

Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

Singh

Manusha

Balaramakrishna

et al. 2022

IJEER

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show abstract

“…Although yielding improved classification performance, these supervised dictionaries neither practice/use any block structure nor explicitly minimize the within-class redundancy. In addition to supervised dictionary, block-structured sparsifying transforms (dictionaries) have not only enhanced the reconstruction ability [33] but also its classification ability [34].…”

Section: Introductionmentioning

confidence: 99%

Enhancing MR Image Reconstruction Using Block Dictionary Learning

et al. 2019

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While representing a class of signals in term of sparsifying transform, it is better to use a adapted learned dictionary instead of using a predefined dictionary as proposed in the recent literature. With this improved method, one can represent the sparsest representation for the given set of signals. In order to ease the approximation, atoms of the learned dictionary can further be grouped together to make blocks inside the dictionary that act as a union of small number of subspaces. The block structure of a dictionary can be learned by exploiting the latent structure of the desired signals. Such type of block dictionary leads to block sparse representation of the given signals which can be good for reconstruction of the medical images. In this article, we suggest a framework for MRI reconstruction based upon block sparsifying transform (dictionary). Our technique develops automatic detection of underlying block structure of MR images given maximum block sizes. This is done by iteratively alternating between updating the block structure of the sparsifying transform (dictionary) and block-sparse representation of the MR images. Empirically it is shown that blocksparse representation performs better for recovery of the given MR image with minimum errors. INDEX TERMS Compressed sensing (CS), sparsifying transforms, magnetic resonance imaging (MRI), block k-singular value decomposition (BLKSVD).

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Enrollee-constrained sparse coding of test data for speaker verification

Kumar

Sinha

2018

Pattern Recognition Letters

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Improved speaker verification using block sparse coding over joint speaker-channel learned dictionary

Cited by 4 publications

References 10 publications

Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

Speaker Identification Analysis Based on Long-Term Acoustic Characteristics with Minimal Performance

Enhancing MR Image Reconstruction Using Block Dictionary Learning

Enrollee-constrained sparse coding of test data for speaker verification

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