Text-Independent Speaker Recognition
6 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Latest papers with no code
Personalizing Keyword Spotting with Speaker Information
Keyword spotting systems often struggle to generalize to a diverse population with various accents and age groups.
A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition
To estimate the IFCs, the TD and the FD features of the speaker's speech are concatenated to build the TD and the FD feature matrix, respectively.
Attention and DCT based Global Context Modeling for Text-independent Speaker Recognition
Second, a 2D-DCT based context model is proposed to improve model efficiency and examine the benefits of signal modeling.
A Lightweight Speaker Recognition System Using Timbre Properties
It also introduces new features that are used for both speaker verification and identification tasks.
JukeBox: A Multilingual Singer Recognition Dataset
We also evaluate the effect of gender and language on speaker recognition performance, both in spoken and singing voice data.
Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques
These high dimensional statistics are then projected to a lower dimensional space where the biometric information is preserved.
Frequency and temporal convolutional attention for text-independent speaker recognition
Majority of the recent approaches for text-independent speaker recognition apply attention or similar techniques for aggregation of frame-level feature descriptors generated by a deep neural network (DNN) front-end.
Probing the Information Encoded in X-vectors
Deep neural network based speaker embeddings, such as x-vectors, have been shown to perform well in text-independent speaker recognition/verification tasks.
Channel adversarial training for cross-channel text-independent speaker recognition
However, these methods always require the collections of different channels from a specific speaker, which is unrealistic to be satisfied in real scenarios.
Deep neural network based i-vector mapping for speaker verification using short utterances
Experimental results using the NIST SRE 2010 dataset show that both methods provide significant improvement and result in a max of 28. 43% relative improvement in Equal Error Rates from a baseline system, when using deep encoder with residual blocks and adding an additional phoneme vector.
