19 results on '"Laskar, Mohammad Azharuddin"'
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2. Deep Learning-Based End-to-End Speaker Identification Using Time–Frequency Representation of Speech Signal
3. CACRN-Net: A 3D log Mel spectrogram based channel attention convolutional recurrent neural network for few-shot speaker identification
4. A Comprehensive Review on Speaker Recognition
5. A Comprehensive Review on Speaker Recognition
6. Formants and Prosody-Based Automatic Tonal and Non-tonal Language Classification of North East Indian Languages
7. HiLAM-aligned kernel discriminant analysis for text-dependent speaker verification
8. Speaker-Phrase-Specific Adaptation of PLDA Model for Improved Performance in Text-Dependent Speaker Verification
9. Modelling multi-level prosody and spectral features using deep neural network for an automatic tonal and non-tonal pre-classification-based Indian language identification system
10. HiLAM-state discriminative multi-task deep neural network in dynamic time warping framework for text-dependent speaker verification
11. Formants and Prosody-Based Automatic Tonal and Non-tonal Language Classification of North East Indian Languages
12. Filterbank Optimization for Text-Dependent Speaker Verification by Evolutionary Algorithm Using Spline-Defined Design Parameters
13. Integrating DNN–HMM Technique with Hierarchical Multi-layer Acoustic Model for Text-Dependent Speaker Verification
14. A Pre-classification-Based Language Identification for Northeast Indian Languages Using Prosody and Spectral Features
15. Raw Waveform Based Speaker Identification Using Deep Neural Networks
16. Deep neural network based two-stage Indian language identification system using glottal closure instants as anchor points
17. A fuzzy‐clustering‐based hierarchical i‐vector/probabilistic linear discriminant analysis system for text‐dependent speaker verification
18. Complementing the DTW based speaker verification systems with knowledge of specific regions of interest
19. A Pre-classification-Based Language Identification for Northeast Indian Languages Using Prosody and Spectral Features
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