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Found 6 papers, 3 papers with code
Towards Matching Phones and Speech Representations
We study two key properties that enable matching, namely, whether cluster centroids of self-supervised representations reduce the variability of phone instances and respect the relationship among phones.
On-Device Constrained Self-Supervised Speech Representation Learning for Keyword Spotting via Knowledge Distillation
Our approach used a teacher-student framework to transfer knowledge from a larger, more complex model to a smaller, light-weight model using dual-view cross-correlation distillation and the teacher's codebook as learning objectives.
Supervised Attention in Sequence-to-Sequence Models for Speech Recognition
Attention mechanism in sequence-to-sequence models is designed to model the alignments between acoustic features and output tokens in speech recognition.
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired.
Ranked #6 on
Speech Separation
on Libri2Mix
(using extra training data)
Interrupted and cascaded permutation invariant training for speech separation
Permutation Invariant Training (PIT) has long been a stepping stone method for training speech separation model in handling the label ambiguity problem.
Ranked #22 on
Speech Separation
on WSJ0-2mix
Improved Speech Separation with Time-and-Frequency Cross-domain Joint Embedding and Clustering
Substantial effort has been reported based on approaches over spectrogram, which is well known as the standard time-and-frequency cross-domain representation for speech signals.
Ranked #24 on
Speech Separation
on WSJ0-2mix
