Text-Independent Speaker Verification
17 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Text-Independent Speaker Verification
Libraries
Use these libraries to find Text-Independent Speaker Verification models and implementationsMost implemented papers
FDN: Finite Difference Network with Hierarchical Convolutional Features for Text-independent Speaker Verification
For example, RawNet and RawNet2 extracted speaker's feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain state-of-the-art performance.
FilterAugment: An Acoustic Environmental Data Augmentation Method
Thus, training acoustic models for audio and speech tasks requires regularization on various acoustic environments in order to achieve robust performance in real life applications.
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
shkim816/temporal_dynamic_cnn
•
•
The temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and results show the necessity to consider phoneme variation within utterances for more accurate and robust text-independent speaker verification.
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for Text-Independent Speaker Verification Explained with Speaker Activation Map
shkim816/decomposed_temporal_dynamic_cnn
•
•
To extract accurate speaker information for text-independent speaker verification, temporal dynamic CNNs (TDY-CNNs) adapting kernels to each time bin was proposed.
The effect of speech pathology on automatic speaker verification -- a large-scale study
tayebiarasteh/pathology_asv
•
•
Navigating the challenges of data-driven speech processing, one of the primary hurdles is accessing reliable pathological speech data.
Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification
danielmengliu/audiovisuallip
•
•
Visual speech (i. e., lip motion) is highly related to auditory speech due to the co-occurrence and synchronization in speech production.
DS-TDNN: Dual-stream Time-delay Neural Network with Global-aware Filter for Speaker Verification
ychenl/ds-tdnn
•
•
To effectively leverage the long-term dependencies of audio signals and constrain model complexity, we introduce a novel module called Global-aware Filter layer (GF layer) in this work, which employs a set of learnable transform-domain filters between a 1D discrete Fourier transform and its inverse transform to capture global context.
