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Found 5 papers, 4 papers with code
The Effect of Batch Size on Contrastive Self-Supervised Speech Representation Learning
We then show that the quality of the pre-trained model depends mainly on the amount of speech data seen during training, i. e., on the product of batch size and number of iterations.
Beyond Neural-on-Neural Approaches to Speaker Gender Protection
Currently, the common practice for developing and testing gender protection algorithms is "neural-on-neural", i. e., perturbations are generated and tested with a neural network.
Speaker and Language Change Detection using Wav2vec2 and Whisper
We investigate recent transformer networks pre-trained for automatic speech recognition for their ability to detect speaker and language changes in speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Training speaker recognition systems with limited data
These subsets are restricted to 50\, k audio files (versus over 1\, M files available), and vary on the axis of number of speakers and session variability.
Fine-tuning wav2vec2 for speaker recognition
This paper explores applying the wav2vec2 framework to speaker recognition instead of speech recognition.
Ranked #2 on
Speaker Recognition
on VoxCeleb1
